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. Author manuscript; available in PMC: 2013 Jun 12.
Published in final edited form as: Drug Resist Updat. 2012 Mar 29;15(0):50–61. doi: 10.1016/j.drup.2012.02.002

Targeting MDR in Breast and Lung Cancer: Discriminating its Potential Importance from the Failure of Drug Resistance Reversal Studies

Laleh Amiri-Kordestani a, Agnes Basseville a, Karen Kurdziel b, Antonio Tito Fojo a, Susan E Bates a,*
PMCID: PMC3680361  NIHMSID: NIHMS367243  PMID: 22464282

Abstract

This special issue of Drug Resistance Updates is dedicated to multidrug resistance protein 1 (MDR-1), 35 years after its discovery. While enormous progress has been made and our understanding of drug resistance has become more sophisticated and nuanced, after 35 years the role of MDR-1 in clinical oncology remains a work in progress. Despite clear in vitro evidence that P-glycoprotein (Pgp), encoded by MDR-1, is able to dramatically reduce drug concentrations in cultured cells, and that drug accumulation can be increased by small molecule inhibitors, clinical trials testing this paradigm have mostly failed. Some have argued that it is no longer worthy of study. However, repeated analyses have demonstrated MDR-1 expression in a tumor is a poor prognostic indicator leading some to conclude MDR-1 is a marker of a more aggressive phenotype, rather than a mechanism of drug resistance. In this review we will re-evaluate the MDR-1 story in light of our new understanding of molecular targeted therapy, using breast and lung cancer as examples. In the end we will reconcile the data available and the knowledge gained in support of a thesis that we understand far more than we realize, and that we can use this knowledge to improve future therapies.

Keywords: MDR-1/Pgp, 99mTc-sestamibi, 18F fluoropaclitaxel, breast cancer, lung cancer, drug penetration

Introduction

The last decade in clinical oncology has been noteworthy for advancing our understanding of the molecular foundations thought responsible for the origin and maintenance of the malignant phenotype. While we still do not understand for every tumor the critical pathways involved, we increasingly understand that both breast and lung cancer, two major solid tumors of adults, represent a collection of molecular subtypes beyond those previously recognized, and no longer assessable as single diseases (Hayes et al., 2006; Perou et al., 2000). Along with this recognition has come an effort to develop therapies directed toward specific molecular aberrations for distinct tumor subtypes. We thus have erlotinib and gefitinib for lung cancers harboring epidermal growth factor receptor (EGFR) mutations and crizotinib for tumors with echinoderm microtubule associated protein like 4-anaplastic lymphoma kinase (EML4-ALK) rearrangements. Breast cancers that express hormone receptors and human epidermal growth factor receptor 2 (HER2) have a different spectrum of agents under development; and there are also agents that may be active in breast cancers that harbor BRCA mutations. And while “cytotoxic agents” remain a cornerstone of many therapies, even for patients with defined mutations, the advent of new, “targeted therapies” has added to the never-answered question of the etiology of drug resistance.

1. Background

Adenosine triphosphate (ATP) binding cassette transporters (ABC) were so named because a conserved ATP binding domain provides the energy required for a conformational change that effectively transfers substrates across the cell membrane to the cell exterior. This energy-consuming process is capable of transferring drug against very steep concentration gradients (Dean et al., 2001). The first discovered human ABC transporter was P-glycoprotein (Pgp), encoded by the MDR1 gene; in all 48 ABC transporters have been identified in humans (Szakacs et al., 2006). These are classified in 7 subfamilies, based on sequencing of the highly conserved ATP-binding domains. Over time, increasing evidence of the involvement of ABC transporters in normal physiology and disease has been gathered, including an ABCG2 variant with impaired uric acid efflux and a role in gout (Woodward et al., 2009), and a recent report that ABCC9 influences sleep requirement (Allebrandt et al., 2011). Many of the ABC transporters have dedicated physiological functions, but the role of several seems to be normal tissue protection – achieved by its expression in the GI tract, kidney, liver, pancreas, and the endothelium of vessels of the brain and testes. Amongst these, the ABC transporters thought most likely to mediate chemotherapy drug resistance are ABCB1 (Pgp/MDR1), ABCC1 (multidrug resistance-associated protein-1 (MRP1)) and ABCG2 (breast cancer resistance protein (BCRP))(Gottesman et al., 2002; Robey et al., 2007).

Given that classical chemotherapeutics are numbered among the substrates for these transporters, including but not limited to doxorubicin, epirubicin, etoposide, paclitaxel, and docetaxel, the potential for a link between drug efflux and drug resistance in the clinic was readily apparent. The discovery by Tsuruo and colleagues (Tsuruo et al., 1981) that verapamil could inhibit the function of Pgp and reverse drug resistance led to clinical trials attempting to reverse drug resistance, beginning with inhibitors already “on the shelf” – verapamil, amiodarone, and cyclosporine – followed by agents such as valspodar and VX710, and finally more potent and specific agents such as tariquidar and zosuquidar. Tariquidar and CBT-1, an orally bioavailable inhibitor, are still in clinical development. The importance of ABC transporters in drug resistance was asked in clinical studies with inhibitors, but due to several considerations including potency of the agents and the design of the trials the question was left unanswered.

2. Clinical trials with inhibitors of Pgp

Tables 1 and 2 outline clinical trials in breast cancer and are consistent with what was observed in lung cancer and other tumor types, including acute myelogenous leukemia (Shaffer, et al., 2012). Many phase I studies tested the safety, tolerability, pharmacokinetic and pharmacodynamics of MDR inhibitors in combination with other agents (selected studies targeting breast cancer populations are shown in Table 1). In early studies the safety of verapamil and r-verapamil in combination with adriamycin and vincristine (Ries and Dicato, 1991; Wilson et al., 1995) and with paclitaxel (Berg et al., 1995; Tolcher et al., 1996) were confirmed. Hematologic toxicity often required a reduction in the dose of the chemotherapeutic agent.

Table 1. Phase I studies in breast cancer.

Modulator Agent Cancer type N Result/ Toxicity Reference
HD Tamoxifen Vinblastine Epithelial tumors 53 DLT: neurotoxicity (Trump et al., 1992)
Verapamil Epirubicin Breast cancer 10 + Significant interaction (Mross et al., 1993b)
R-verapamil Paclitaxel Breast cancer 6 (Berg et al., 1995)
R-verapamil Paclitaxel Breast cancer 34 Paclitaxel alone: 6/34 18% PR (Tolcher et al., 1996)
29 Crossover: 0/29 PR Hematologic toxicity from paclitaxel + r-verapamil
Valspodar PEG-LD Advanced cancer 14 1 PR in breast and ovariancarcinoma.No DLTs. (Fracasso et al., 2005)
Sulindac Epirubicin Advanced cancer 17 2/15 PR (malignant melanoma and breast cancer)DLT 800 mg: 1 renal impairment, 1 fatal haemoptysis in lung cancer (O'Connor et al., 2007)
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HD: high dose

DLT: dose limiting toxicity

PR: partial response

N: number of patients

Table 2. Phase II studies in breast cancer.

Inhibitor Agent N Result Reference
Verapamil Adriamycin + Vincristine 16 3 PR (21%). Median OS 6 m (Ries and Dicato, 1991)
Bepridil Anthracycline 14 3 SD (van Kalken et al., 1991)
Trifluoperazine Doxorubicin 20 9 PR (45%). Median DOR17 wks (Budd et al., 1993)
Verapamil (+/-) Epirubicin 26 - EPI + verapamil: CR (4%), 7 PR (29%) (Mross et al., 1993a)
25 - EPI alone: 8 PR (28%)
Quinidine Epirubicin 106 - EPI + placebo: 6% CR, 38% PR; OS: 59 wk (Wishart et al., 1994)
107 - EPI + quinidine: 4% CR, 39% PR; OS: 47 wk
Amiodarone Doxorubicin or vinblastine 33 9/33 PR (Bates et al., 1995)
Lonidamine Epirubicin 45 EPI alone: 6 CR, 14 PR; DOR 6.5 mo (Lopez et al., 1995)
25 Crossover: EPI + lonidamine: 5 PR; DOR 7 mo
Trifluoperazine Vinblastine 16 1 PR (6%). Lasted 16 wks (Murren et al., 1996)
Dexverapamil Epirubicin 25 4 PR (17%). Lasted 3, 8, 11, 11+ m (Lehnert et al., 1998)
Dexverapamil Anthracycline 20 2 PR (10%). Lasted for 6 m (Warner et al., 1998)
Biricodar Paclitaxel 35 4 PR (11.4%). DOR 5.5 mo (Toppmeyer et al., 2002)
Tariquidar Doxorubicin or taxane-containing regimens 17 1 PR (Pusztai et al., 2005)
Valspodar Paclitaxel 34 2/34 CR 6%; 5/34 PR 15 % (Carlson et al., 2006)
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CR: complete response

PR: partial response

SD: stable disease

OS: overall survival

EPI: epirubicin

LND: lonidamine

N: number of patients

DOR: Duration of response

The efficacy of combining an MDR inhibitor with different chemotherapy agents in metastatic breast cancer was studied in several phase II and phase III trials (Table 2). Two randomized Phase II clinical trials compared epirubicin to epirubicin with either verapamil (Mross et al., 1993a) or quinidine (Wishart et al., 1994). Neither of these studies could show a significant improvement in the response rate (Table 2). While in similar patients with metastatic breast cancer, phase III trials gave dissimilar results. The first trial randomized 99 patients to receive vindesine 3 mg/m2 on days 1 and 10 with continuous infusion 5-FU on days 1 through 10 of each 28 day cycle with or without oral verapamil 240 mg/day. Patients treated with verapamil had a longer overall survival 323 vs 209 days (p = 0.036) and a higher response rate, 27% vs 11% (p = 0.04) (Belpomme et al., 2000). However, verapamil is not a potent Pgp inhibitor, and a second study with a higher potency agent failed to demonstrate a similar improvement. Saeki et al conducted a phase III randomized double blind control study of six cycles of CAF (100 mg cyclophosphamide administered orally on days 1 through 14, together with 25 mg/m2 adriamycin and 500 mg/m2 fluorouracil administered intravenously on days 1 and 8) with or without 900 mg dofequidar administered orally on days 1 - 14 in 221 patients with metastatic breast cancer. Although both the overall response rates of 42.6% and 53.1% (P = 0.077) and median progression-free survivals of 241 days and 366 days (P =0.145) for CAF alone and CAF plus dofequidar, respectively, suggested a benefit from adding dofequidar, the results did not reach statistical significance (Saeki et al., 2007).

Multiple explanations including trial design can be offered for the failure of these trials to convincingly show clinical benefit. One is that many of the studies used inhibitors that were low in potency. Additionally, toxicities of the earlier agents such as calcium channel blockers (verapamil and other analogues) and amiodarone, prevented dose escalation and yielded an ineffective dose, almost certainly true of the verapamil studies (Mross et al., 1993a; Wishart et al., 1994). Later agents such as valspodar required reduction in the dose of the anticancer agent due to CYP3A4-related pharmacokinetic interactions, a strategy that undermined any potential value of the combination, as sub-therapeutic peak concentrations likely resulted (Bates et al., 2004). It is also likely that, if drug transporters are important resistance mechanism in breast cancer, they are important in only a subset of tumors. The inhibitors were quickly developed and reached the clinic before there was understanding that clinical trials need to enrich for the patient population under study. The development of trastuzumab in breast cancer was illustrative. In an unselected population, a low response rate of 26% (5% in FISH- vs. 41% FISH+) was noted, and only in HER2+ tumors did clinical benefit clearly emerge (Vogel et al., 2001). Similarly, in an unselected population of patients with lung cancer, the EGFR inhibitor, erlotinib, had a response rate of 8.9% and only in tumors harboring a mutant EGFR did clinical benefit emerge(Shepherd et al., 2005). These examples show that the failure to select patients for enrollment alone could have doomed the strategy to failure. However, enrichment of a patient population for the trial of an inhibitor of drug efflux would have required a reliable assay that indicated MDR as a dominant mechanism of resistance, and for solid tumors this was never developed.

A further confounding problem with design is that while some were randomized studies (Mross et al., 1993a; Wishart et al., 1994), a larger number used a run-in phase or cross-over design (Bates et al., 1995) in which patients who initially received the chemotherapy agents alone were then treated with an MDR inhibitor, in some studies only after clear progression on chemotherapy had been documented (Warner et al., 1998). If a drug transporter were important, one could envision these designs leading to greater resistance mediated by both the transporter as well as other mechanism that could emerge. Development of additional mechanisms of tolerance could also undermine as targeting a single mechanism resulted in less efficacy (Germano and O'Driscoll, 2009).

Not encountered in the breast cancer trials was a problem noted in trials in leukemia and in lung cancer – increased toxicity in the experimental arm. The observation that leukemias often express high levels of Pgp, and that expression correlates with poor survival, together with the continued poor outcome of patients with acute myeloid leukemia, prompted the conduct of a large number of randomized studies with Pgp inhibitors (Shaffer et al., 2012). Notably, higher rates of mortality and adverse events were observed in the experimental arms of several studies. A similar finding was observed with the Pgp inhibitor, tariquidar, in two large randomized Phase III trials in patients with non-small cell lung cancer. Both trials closed early for adverse events and mortality rates that would have made it impossible to demonstrate any survival benefit from the combination. While not proven, inhibition of Pgp in bone marrow stem cells or early progenitor cells and in drug metabolizing organs such as the liver are viewed as the most likely explanations for the increased toxicity.

Other variables that may have contributed to the failure of clinical trials may have been the inclusion of patients with polymorphic variants of transporters that we now understand can impair function. These polymorphisms may make bone marrow stem cells or early progenitors more susceptible to Pgp inhibition (Cascorbi, 2006). Interestingly, a synonymous polymorphism in ABCB1 -- 3435C>T SNP, together with two others, 2677G>T/A, and/or 1236C>T, comprise a haplotype that in general has been associated with impaired protein function. The mutation at the 3435 C>T SNP site may cause ribosome stalling and different speeds of protein translation, impacting protein folding (Fung and Gottesman, 2009). Other transporters potentially involved in drug resistance are also subject to polymorphic variation. For example, a variant ABCG2, C421A, replaces a glutamine with a lysine at amino acid residue 141 and is associated with impaired protein trafficking so that the protein is degraded rather than trafficked to the cell surface (Furukawa et al., 2009; Morisaki et al., 2005). Variants encoding stop codons have also been described (Saison et al., 2012). Such polymorphisms, unknown during early clinical trials, could confound results by including some patients whose tumors will not develop significant drug transporter-mediated resistance but whose bone marrow might be more sensitive to chemotherapy substrates when combined with a transport inhibitor. Although a hypothesis, it is possible that selection of patients could have benefitted in two directions – identifying patients whose tumors had high expression of Pgp, who may have benefited from addition of an inhibitor and those whose tumors had low expression and were not likely to benefit but instead had greater toxicity.

These comments make clear that the trials were conducted too early, with insufficient understanding. Despite multiple trials, few actually confirmed expression of Pgp in tumor tissue, none required expression for enrollment and none demonstrated inhibition of drug efflux and increased drug accumulation in tumors with addition of the Pgp inhibitor. No trials demonstrated that the Pgp inhibitor was able to penetrate tumor tissue. No trials evaluated genotype to determine the impact of polymorphic variants. Despite the lack of such pharmacodynamic data, the clinical results were considered by many to be conclusive and interest in ABC transporters as a mechanism of drug resistance faded.

3. Beyond Pgp inhibitors: ABC transporter expression and correlation with clinical outcomes

Despite the largely negative results in clinical trials summarized above, expression studies have repeatedly shown correlations with clinical outcome. In leukemia three decades of data support an adverse outcome for patients whose leukemias express high levels of Pgp. In breast and lung cancer, the data are also fairly compelling. A 2005 meta-analysis of Pgp expression in breast cancer concluded that a significant number of breast cancer samples demonstrate Pgp expression, that expression is increased after chemotherapy, and that expression correlates with a worse response to treatment (Clarke et al., 2005). Even in the last decade, as interest in trials has waned, studies in breast cancer examining expression of the three ABC transporters most often linked to drug resistance have again reported that expression is often, although not always associated with adverse outcome, as shown in Table 3A. Similarly, ABC transporter expression in lung cancer has been associated with poor outcome (Stewart, 2010). The most recent decade of studies shown in Table 3B confirms that association. The question is whether expression is related to decreased drug accumulation or is a marker for another feature of poor outcome, such as invasiveness (Colone et al., 2008; Mignogna et al., 2006).

Table 3. Expression studies for MDR-1/Pgp, ABCC1/MRP, and ABCG2/BCRP: Correlation with clinical outcome.

n * treatment ** MDR1 / Pgp / ABCB1 *** Correlation with outcome **** MRP1 / ABCC1 *** Correlation with outcome**** BCRP / ABCG2 *** Correlation with outcome**** Reference
TABLE 3A: Breast cancer
196 high ABCG2 lymph node metastasis (p= .049), stage (p=.015) HER2 expression (p=.001) (Xiang et al., 2011)
25 A or E Pgp induction shorter PFS (p=.0004)
shorter OS (p=.0025)		 MRP1 induction NS for PFS
NS for OS		 (Atalay et al., 2008)
90 mRNA NS for clinical or pathological characteristics (Vaclavikova et al., 2008)
25 FAC or FEC Pgp induction
Pgp positive		 poorer RR (p<.001)
poorer RR (p<.001)		 MRP1 induction
MRP1 positive		 NS for RR
NS for RR		 (Atalay et al., 2006)
171 FEC +/-radiotherapy +/-hormonal therapy mRNA NS for RFS
NS for OS		 mRNA NS for RFS
NS for OS		 (Moureau-Zabotto et al., 2006)
21 FEC + paclitaxel mRNA NS for RR mRNA NS for RR mRNA NS for RR (Park et al., 2006)
87 Pgp neg correlation with histological grade MRP1 NS for histological grading status (Rybarova et al., 2006)
50 FAC Pgp positive poorer RR (p<.05) (Chintamani et al., 2005)
516 CMF MRP1 positive shorter RFS (p=.002)
shorter OS (p=.02)		 (Filipits et al., 2005)
tamoxifen+goserelin MRP1 positive NS for OS and RFS
104 CMF or tamoxifen +/- radiotherapy +/-hormonal therapy high Pgp higher grade (p<.001)
shorter OS (p<.0001)
shorter PFS (p<.0001)		 (Surowiak et al., 2005)
177 CMF high Pgp NS for RFS
NS for OS		 high MRP1 shorter RFS (p=.0181)
shorter OS (p=.0171)		 (Larkin et al., 2004)
59 CMF+ FAC-FEC high mRNA shorter OS (p=.05)
shorter PFS (p<.001)		 high mRNA NS for OS
NS for RFS		 high mRNA NS for OS
NS for PFS		 (Burger et al., 2003)
FAC-FEC high mRNA shorter OS (p<.001)
shorter PFS (p=.007)		 high mRNA shorter OS (p=.056)
shorter PFS (p=.04)		 high mRNA NS for OS
NS for PFS
CMF high mRNA NS for OS or PFS high mRNA NS for OS or PFS
80 CMF, anthracycline-based or taxane-based chemotherapy Pgp pre-vs post-chemo higher expression after treatment (p<.001) MRP1 pre-vs post-chemo higher expression after treatment (p<.001) (Rudas et al., 2003)
Pgp positive pre-chemo positive lymph nodes (p=.008) MRP1 positive pre-chemo distant metastases and shorter PFS (p=.02)
52 anthracycline mRNA/ ABCG2 NS for PFS or RR no anthracyclin-induced expression (Faneyte et al., 2002)
n * treatment ** MDR1 / Pgp / ABCB1 *** Correlation with outcome **** MRP1 / ABCC1 *** Correlation with outcome**** BCRP / ABCG2 *** Correlation with outcome**** Reference
TABLE 3B: Lung Cancer
49 NSCLC gefitinib ABCG2 positive shorter PFS (p=.026)
shorter OS (p=.005)		 (Chen et al., 2011)
94 NSCLC gefitinib ABCG2 NS for RR, time to progression and OS (Lemos et al., 2011)
21 both cisplatin-etopside and/or CAVE mRNA NS for OS
NS for RR		 mRNA NS for OS
NS for RR		 high mRNA shorter OS (p=.034)
NS for RR		 (Rijavec et al., 2011)
46 NSCLC cisplatin-based chemotherapy high mRNA poorer RR (p=.032),
shorter PFS (p=.043)
shorter OS (p=.019)		 mRNA NS for RR
NS for PFS
NS for OS		 (Li et al., 2010)
81 CLELC PDT high ABCG2 poorer RR (P = .04) for lesion >1cm (Usuda et al., 2010)
130 SCLC platinum-based chemotherapy Pgp positive NS for RR
NS for FPS		 MRP1 positive NS for RR
NS for PFS		 ABCG2 positive poorer RR (p=.026)
shorter PFS (p=.0103)		 (Kim et al., 2009)
60 NSCLC cisplatin-based chemotherapy high mRNA shorter PFS (p=.034)
shorter OS (p=.021)		 mRNA NS for PFS
NS for OS		 (Li et al., 2009b)
66 NSCLC cisplatin-based chemotherapy high mRNA poorer RR (p=.046)
shorter PFS (p=.012)
shorter OS (p=.017)		 mRNA NS for RR
NS for PFS
NS for OS		 (Li et al., 2009a)
156 NSCLC platinum-based chemotherapy high ABCG2 shorter OS (p=.02)
NS for RR or PFS		 (Ota et al., 2009)
101 both not specified Pgp positive poorer RR (p<.05) (Paredes Lario et al., 2007)
61 SCLC platinum-based/CAV Pgp positive poorer RR (p=.03) MRP1 positive NS for RR (Ushijima et al., 2007)
17 SCLC cisplatin-etoposide high Pgp poorer RR (p<.0001) high MRP1 poorer RR (p=.0002) (Triller et al., 2006)
40 SCLC cisplatin-etoposide Pgp positive poorer RR (p<0.01) MRP1 positive poorer RR (p<0.01) (Yeh et al., 2005)
72 NSCLC platinum-based chemotherapy Pgp NS for RR
NS for OS
NS for PFS		 MRP1 NS for RR
NS for OS
NS for PFS		 ABCG2 positive poorer RR (p=.08)
shorter PFS (p=.0003)
shorter OS (p=.004)		 (Yoh et al., 2004)
50 NSCLC paclitaxel Pgp positive poorer RR (p<.05) (Yeh et al., 2003)
50 SCLC cisplatin-etoposide high Pgp + high MRP1 poorer RR (p<.05) high MRP1 + high Pgp poorer RR (p<.05) (Hsia et al., 2002)
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*

n, number of patients; “SCLC”, “NCSLC” or “both” refer to study on small cell lung cancer, non-small cell lung cancer, or both cancers, respectively. CLELC: centrally located early lung cancer.

**

A: adriamycin (doxorubicin), C: cyclophosphamide, E: epirubicin, F: fluorouracil, M: methotrexate, V: vincristine; PDT: photodynamic therapy

***

Pgp, MRP1 and ABCG2 refer to the protein product. “High protein” and “protein positive” indicate that a threshold was set for high/low or for positivity/negativity, respectively

****

OS, overall survival; PFS, progression-free survival; RR, response rate; NS, no significant correlation

While overexpression of Pgp and other ABC transporters in tumor tissue has been associated with outcome, the mechanism underlying that overexpression has not been fully explored. Apart from a few specific examples, such as gene rearrangement and capture of the MDR1 gene by a constitutively active promoter (Mickley et al., 1997), overexpression is related to the state of differentiation or as a response to drug exposure - both a consequence of epigenetic regulation. We observed that MDR1 induction is one of the most consistent changes in gene expression that occurs following the histone acetylation that results from addition of histone deacetylase inhibitors (Bates et al., 2010). MDR1 transcription is also upregulated by the histone methyltransferase mixed lineage leukemia 1 protein (MLL 1), through the activating methylation at lysine 4 of histone H3 (Huo et al., 2010). Mutated, rearranged or duplicated, MLL is leukemogenic and may concurrently upregulate MDR1. It is likely that a parallel mechanism exists in solid tumors. Other epigenetic changes associated with increased gene expression, such as MDR1 promoter CpG hypomethylation, have been observed following chemotherapy (Baker et al., 2005). Finally, chemotherapeutic agents activate the pregnane X receptor, a master transcription factor for drug metabolizing enzymes, and a mediator of MDR1 transcription. Induction of MDR1 by this mechanism has been observed in breast and prostate cancer cells (Chen et al., 2009; Chen et al., 2007). Together, these studies show that MDR1, and by extension other ABC transporters, are regulated genetically and epigenetically, and suggest that overexpression may occur both during oncogenesis and in response to the administration of chemotherapeutic agents.

4. The complexity of drug accumulation

A less well studied explanation for the failure of clinical trials to confirm the MDR hypothesis is that drug transporters may simply be one of several factors affecting drug accumulation in cells with additional factors affecting drug penetration into tumors. Indeed, although numerous animal models have been evaluated, the mechanisms underlying drug penetration into tumors have not been well studied in patients. Three early studies that measured doxorubicin levels in breast cancer reported a disturbingly broad range. Cummings et al reported a mean doxorubicin concentration of 819 ± 482 ng/g at 30 minutes, while Stallard noted a 7-fold range from 220 – 1590 ng/g at one hour, and Rossi a 16-fold range from 1.86 to 30 ug/g at 24 hours (Cummings and McArdle, 1986; Rossi et al., 1987; Stallard et al., 1990). In a subsequent study, Lankelma and colleagues reported steep doxorubicin gradients just microns away from a tumor's vasculature and showed these gradients varied among patients (Lankelma et al., 1999). A more recent evaluation of paclitaxel in cervical and ovarian cancers demonstrated median concentrations of 324 and 305 ng/g, respectively; with concentrations (ng/g) at the 25th and 90th percentile concentrations of 160 and 736.8 for cervical cancer and 185 and 862 for ovarian cancer [(Koshiba et al., 2009), and Hisato Koshiba, personal communication]. Given the steep dose-response curves for most cancer chemotherapies, these data strongly suggest adequate drug concentrations may not be achieved in some tumors and mandate a better understanding of factors responsible for drug penetration and persistence in tumor tissue. Yet we still do not have a means of assessing chemotherapy gradients, nor altering them in patients. It is thought that multiple factors, among them pH, interstitial pressure, and hypoxia, influence these gradients and that these factors are linked at least in part with the disordered vasculature that is the hallmark of cancer. Thus, drug transporter expression may influence drug accumulation in cells and possibly in tissues, but likely is only one of multiple factors regulating penetration into tumor tissue.

5. Imaging drug uptake and accumulation in tissues and in tumors

A first assumption in cancer therapy is that a drug always reaches its target. We infer this when we see clinical responses, and then assume other mechanisms account for treatment failure. But in fact we know very little about the extent of variation in drug penetration and to what extent that can account for treatment failure. An evolving strategy for evaluating drug uptake in tumors is radiolabeled imaging of either anticancer drugs or surrogates. There are considerable data regarding 99mTc-sestamibi, a radionuclide imaging agent approved by the FDA for cardiac imaging and for its ability to detect breast cancers. 99mTc-sestamibi has been shown to be promising in the detection of microcalcifications (Fondrinier et al., 2004), occult breast carcinomas (Coover et al., 2004), breast cancer (Sampalis et al., 2003), as well as in staging, axillary lymph node evaluation (Myslivecek et al., 2004; Ozulker et al., 2010; Zhou et al., 2009) and sentinel node mapping (Arcan et al., 2005; Sadeghi et al., 2010). Although 99mTc-sestamibi is approved by the FDA for breast imaging, current evidence does not support its use for breast cancer screening [NCCN guidelines v2 2011], in part due to the difficulty of detecting lesions smaller than 10 mm.

Multiple small studies have evaluated imaging with 99mTc-sestamibi, or a similar agent tetrafosmin, as a surrogate for chemotherapy in patients with lung cancer, finding that imaging uptake was highly associated with paclitaxel-based chemotherapy response (Ceriani et al., 1997; Fuster et al., 2003; Komori et al., 2000; Mohan and Miles, 2009; Nishiyama et al., 2000; Shih et al., 2003; Yuksel et al., 2002). A meta-analysis concluded the test had predictive value for this purpose (Mohan and Miles, 2009). Given that sestamibi and tetrofosmin are substrates for both Pgp and MRP1-mediated efflux, co-expression in lung cancer could reduce accumulation. Similarly in breast cancer, sestamibi washout has been correlated with response to neoadjuvant chemotherapy (Alonso et al., 2002; Ciarmiello et al., 1998; Mankoff et al., 1999; Sciuto et al., 2002), and linked to Pgp expression (Sun et al., 2000).

The functional activity of the Pgp transporter in a tumor can be measured with 99mTc-sestamibi scans, if coupled with administration of a MDR efflux inhibitor. This has been evaluated in solid tumors (Abraham et al., 2009; Agrawal et al., 2003; Bates et al., 2004) including breast cancer (Pusztai et al., 2005; Sun et al., 2000). We observed marked heterogeneity of uptake in sestamibi in lung cancer, with minimal change following tariquidar (Kelly et al., 2010), presumably due to non-Pgp factors, such as hypoxia, limiting uptake.

Despite potential utility, the limitations of single photon imaging with 99mTc-sestamibi have kept it from entering clinical practice. The optimal imaging parameters (i.e. imaging times, retention index calculation methods) have not been established, and the overall predictive value of these studies has not been validated to warrant change in clinical therapy. Single photon imaging is limited by overall low count rate requiring relatively long imaging times. Furthermore, routine dynamic imaging is performed by planar imaging; resulting in only relative count data (i.e. not truly quantitative).

In contrast, positron emission tomography (PET) imaging is quantitative, provides improved spatial resolution (∼5 mm), and has high-count rate sensitivity. Carbon-11 (11C), a PET radionuclide, has been used to label several Pgp substrates including verapamil (Hendrikse et al., 2001), colchicine (Levchenko et al., 2000), daunorubicin (Elsinga et al., 1996), loperamide and [N-methyl]N-desmethyl-loperamide (Lazarova et al., 2008). Radiolabeled therapy agents have also been explored including [11C]paclitaxel (Ravert et al., 2002), [111In]paclitaxel (Li et al., 1997), and [11C]docetaxel (van Tilburg et al., 2004). However, the 20.4-minute physical half-life of [11C] and corresponding need for on-site synthesis limits the clinical utility of [11C]-tracers.

Paclitaxel, a chemotherapeutic agent and a Pgp substrate, has been labeled with [18F], a positron emitter with a longer half-life which should allow imaging of slowly changing physiological phenomena, and like 99mTc-sestamibi, offers the possibility of evaluating drug uptake as well as Pgp function. Paclitaxel is not a substrate for multidrug resistant protein (MRP) (Huang et al., 1997), is a neutral compound, and does not require an electropotential gradient for intracellular retention. The overall 18F fluoropaclitaxel (FPAC) kinetics are dependent on microtubule specific binding, non-specific FPAC binding and Pgp-related efflux.

Preclinical animal FPAC imaging data showed a similar biodistribution between unlabeled fluoropaclitaxel and paclitaxel (Gangloff et al., 2005; Jagoda et al., 2002; Kiesewetter and Eckelman, 2001; Kurdziel et al., 2007; Schinkel, 1998). In a human breast cancer xenograft model, FPAC was shown to predict chemotherapeutic response (Hsueh et al., 2006). FPAC PET was used in vivo to image and quantify Pgp inhibition following the intravenous administration of tariquidar (Kurdziel et al., 2011). To date, three normal volunteers and three breast cancer patients have been imaged with FPAC. While the overall tumor uptake was small, there was low background activity in the chest, breast, brain, head and neck, making even small differences in FPAC accumulation apparent (Figure 1).

Figure 1.

Figure 1

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FPAC imaging of a patient with breast cancer. Fused PET/CT (top row, axial and coronal views) and PET (bottom row, axial and coronal views) of a female patient with breast cancer 80 minutes after the injection of 6.1mCi 18F fluoropaclitaxel (FPAC). Right breast tumor (arrow) showing increased FPAC uptake (SUV of 1.3). Note absence of uptake in the brain due to the blood-brain-barrier (BBB) (a portion of the pituitary gland outside the BBB seen (solid arrow head)). Physiologic uptake in the heart, liver, bowel and bone marrow are also seen, as is residual tracer in the vasculature of the injected arm (brackets). Due to the extensive hepatic clearance and subsequent excretion into bowel, the diagnostic value of FPAC PET in the abdomen and pelvis is limited. A pilot study of FPAC PET in patients with a renal, adrenal, lung and breast cancer patients is ongoing (http://clinicaltrials.gov/ct2/show/NCT01086696).

6. Drug uptake and accumulation in sanctuary sites - the CNS as a paradigm

Another aspect of drug resistance is the existence of sanctuary sites such as the central nervous system (CNS), an example of an environment protected from both the toxic and beneficial effects of chemotherapeutics (Lin et al., 2004; Steeg et al., 2011). For patients whose tumors express HER2, the increase in survival that has resulted from the use of HER2 targeting agents has been complicated by the emergence of CNS metastases (Dawood et al., 2009; Eichler et al., 2008; Kirsch et al., 2005; Ricciardi and de Marinis, 2010),(Brufsky et al., 2011). This is accompanied by morbidity associated with the CNS disease itself (Witgert and Meyers, 2011), as well as the adverse effects of treatment, which centers on surgery and radiation therapy (Platta et al., 2010). Often occurring in patients whose systemic disease is well-controlled, it is assumed that the metastases arise from dormant tumor cells that have crossed the blood brain barrier (BBB) and have failed to be eliminated or controlled by chemotherapeutics that do not have access to the CNS (Eichler et al., 2011). The BBB separates circulating blood from the extracellular fluid of CNS. It consists of tight junctions around capillaries, perivascular astrocytes, as well as a number of transporters including ABCB1 and ABCG2 (Abbott et al., 2006) (Deeken and Loscher, 2007).

The contribution of transporters to the BBB has been evaluated in mouse models in which ABCB1 and ABCG2 have been deleted. Early experiments with mice lacking only the ABCB1 orthologues showed minimal impact on brain uptake, suggesting the tight junctions or other facets were more important. Subsequent studies in mice lacking both ABCB1 and ABCG2 revealed a dramatic impact on brain uptake with the obvious caveat that we cannot be certain this is predictive of their role in the human BBB. Figure 2 compiles several reports on the effect of the double knockout on uptake of anticancer drugs in the CNS (de Vries et al., 2007; Kodaira et al., 2010; Lagas et al., 2010; Poller et al., 2011; Polli et al., 2009; Tang et al., 2012). It is noteworthy that most of the agents included are targeted agents. Uptake of drugs known to be substrates for drug transporters including lapatinib, topotecan, mitoxantrone, sunitinib, sorafenib, and axitinib showed minimal alteration when either the ABCB1 or ABCG2 gene had been deleted, but 10 – 25-fold increases in brain concentrations when both genes were deleted. While providing proof of concept that ABC transporters could limit drug uptake, these studies also demonstrated the protection conferred by redundancy.

Figure 2.

Figure 2

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The impact of ABC transporters on CNS uptake in murine knockout studies. Data from 6 separate studies were compiled to generate the bar graphs shown (de Vries et al., 2007; Kodaira et al., 2010; Lagas et al., 2010; Poller et al., 2011; Polli et al., 2009; Tang et al., 2012). All of the studies employed mice bearing knockout of ABCB1a/b, ABCG2, or both ABCB1a/b and ABCG2 mice. Brain concentrations were reported as relative to wild type and were measured at different timepoints – lapatinib, 24 h; topotecan, 24 h; mitoxantrone, 2 h; axitinib, 1 h; sorafenib, 6 h; and sunitinib, 6 h. Mitoxantrone data were expressed as Cbrain/Cplasma and estimated from Figure 4 in the reference (Kodaira et al., 2010), while topotecan data were reported as area under the concentration curve (AUC) (de Vries et al., 2007).

Although there is evidence the BBB is only partially intact in metastatic tumors (Taskar et al., 2011), studies in patients with CNS metastases suggest brain concentrations are limiting. For example, response rates to lapatinib of only 2.6 and 6% were observed in CNS metastases due to breast cancer (Lin et al., 2008; Lin et al., 2009). Although in a small series of patients with lung cancers bearing EGFR mutations, erlotinib given on a weekly “high dose” schedule induced a better response rate at 67% (Grommes et al., 2011). Together these studies suggest prevention or treatment of metastatic disease in the CNS may be one potentially important area for the study of efflux inhibitors or novel drugs developed to avoid drug transporters.

7. Perspective

Unless there is an unaccountable level of publication bias influencing the findings discussed above, the fundamental conclusion seems to be that expression of Pgp or other MDR transporters and associated drug efflux in a tumor is bad, but that so far Pgp has not been successfully translated to a therapeutic target. Unfortunately, too many have taken this to mean that Pgp is not important clinically, a conclusion not supported by the wealth of data both in pre-clinical models and most importantly in clinical studies. Many analogies come to mind, all of which illustrate the folly of reaching such a conclusion. The taxanes and the vinca alkaloids for example, specifically target tubulin and the microtubules, but are effective in only a fraction of patients. Notably, they are inactive in colon cancer. To conclude from this that microtubules are not essential for colon cancer would be at best misguided and certainly incorrect. Similarly, the recent near universal disappointment with agents targeting mitosis including inhibitors of the aurora kinases, mitotic spindle protein and polo-like kinase would never be interpreted as evidence that mitosis is not important in cancer. So too can the only conclusion drawn from the data with Pgp be that we have been, to date, unable to inhibit its function effectively in tumors or that its inhibition alone has not been sufficient – not that Pgp is not important.

This distinction is important as one considers the way forward in this field of research. Because studies correlating expression with poor outcomes have usually examined cancers treated with drugs we consider Pgp substrates, one can conclude that the poorer outcomes of those patients whose tumors express Pgp can be in part explained by less effective chemotherapy. While we cannot exclude that Pgp might be a marker or surrogate of other more important resistance mechanisms or of “bad tumor biology”, it is also possible, indeed most likely, that Pgp by conferring resistance is in part responsible for the poorer outcomes. Together, these data can be taken as compelling evidence to develop agents for cancer that are not substrates for Pgp or other MDR transporters. Additional benefits from developing agents that are not substrates for multidrug transporters would include enhanced oral bioavailability and increased permeability into the brain and other sanctuary sites where penetration is prevented by ABC transporters. The latter has been seen as a limitation of traditional cytotoxic agents such as the taxanes and other microtubule targeting agents. Novel analogs now in development such as GRN1005 (http://clinicaltrials.gov/ct2/show/NCT01480583), a paclitaxel drug conjugate, offer the hope of overcoming this obstacle. Similarly the tyrosine kinase inhibitors (TKIs) have proven to be substrates for transporters as a class and clinical data suggest brain penetration is limited (Brozik et al., 2011). A TKI that is not a substrate for ABC transporters could be a useful addition to the armamentarium.

As for continuing to evaluate Pgp or other MDR transporter as a therapeutic target, any such trials should enroll patients only when transporter expression is documented and blood is stored for genotyping. An effort should be directed toward developing a real test for transporter expression. At this time a specific recommendation for a particular antibody-based assay for Pgp cannot be made – one of the most commonly used antibodies was shown a decade ago to have cross-reactivity with c-erbB2 (Liu et al., 1997). Measuring expression by PCR has been problematic – the greater sensitivity of that assay renders most tumors positive for expression; a cut-off of significance has not been determined in any tumor type, and RNA expression would not solve the potential role of protein variants in altering function. These difficulties reinforce the point that a major effort should be made to develop imaging agents that would allow assessment of drug uptake, and to understand rate-limiting factors of uptake.

We will probably come to understand drug transporter expression as part and parcel of the malignant phenotype, sometimes less important than other features and sometimes a dominant mechanism of resistance. In the end, personalized medicine will be about making this distinction, and 35 years on, we might be able to convincingly determine the role of drug uptake in clinical drug resistance.

Footnotes

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