Chemotherapeutics are the most effective treatment for metastatic tumours. However, the ability of cancer cells to become simultaneously resistant to different drugs--a trait known as multidrug resistance--remains a significant impediment to successful chemotherapy. Three decades of multidrug-resistance research have identified a myriad of ways in which cancer cells can elude chemotherapy, and it has become apparent that resistance exists against every effective drug, even our newest agents. Therefore, the ability to predict and circumvent drug resistance is likely to improve chemotherapy.

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Multidrug resistance in cancer: role of ATP–dependent transporters

Resistance to chemotherapy and molecularly targeted therapies is a major problem facing current cancer research. The mechanisms of resistance to 'classical' cytotoxic chemotherapeutics and to therapies that are designed to be selective for specific molecular targets share many features, such as alterations in the drug target, activation of prosurvival pathways and ineffective induction of cell death. With the increasing arsenal of anticancer agents, improving preclinical models and the advent of powerful high-throughput screening techniques, there are now unprecedented opportunities to understand and overcome drug resistance through the clinical assessment of rational therapeutic drug combinations and the use of predictive biomarkers to enable patient stratification.

Cancer drug resistance: an evolving paradigm

The contribution of tumorigenic stem cells to haematopoietic cancers has been established for some time, and cells possessing stem-cell properties have been described in several solid tumours. Although chemotherapy kills most cells in a tumour, it is believed to leave tumour stem cells behind, which might be an important mechanism of resistance. For example, the ATP-binding cassette (ABC) drug transporters have been shown to protect cancer stem cells from chemotherapeutic agents. Gaining a better insight into the mechanisms of stem-cell resistance to chemotherapy might therefore lead to new therapeutic targets and better anticancer strategies.

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Tumour stem cells and drug resistance

Effective treatment of metastatic cancers usually requires the use of toxic chemotherapy. In most cases, multiple drugs are used, as resistance to single agents occurs almost universally. For this reason, elucidation of mechanisms that confer simultaneous resistance to different drugs with different targets and chemical structures - multidrug resistance - has been a major goal of cancer biologists during the past 35 years. Here, we review the most common of these mechanisms, one that relies on drug efflux from cancer cells mediated by ATP-binding cassette (ABC) transporters. We describe various approaches to combating multidrug-resistant cancer, including the development of drugs that engage, evade or exploit efflux by ABC transporters.

Targeting multidrug resistance in cancer

Membrane transporters can be major determinants of the pharmacokinetic, safety and efficacy profiles of drugs. This presents several key questions for drug development, including which transporters are clinically important in drug absorption and disposition, and which in vitro methods are suitable for studying drug interactions with these transporters. In addition, what criteria should trigger follow-up clinical studies, and which clinical studies should be conducted if needed. In this article, we provide the recommendations of the International Transporter Consortium on these issues, and present decision trees that are intended to help guide clinical studies on the currently recognized most important drug transporter interactions. The recommendations are generally intended to support clinical development and filing of a new drug application. Overall, it is advised that the timing of transporter investigations should be driven by efficacy, safety and clinical trial enrolment questions (for example, exclusion and inclusion criteria), as well as a need for further understanding of the absorption, distribution, metabolism and excretion properties of the drug molecule, and information required for drug labelling.

https://www.njacs.org/wp-content/docs/2010-10-DrugMet-R-Evers.pdf

Membrane transporters in drug development.

MCF-7/AdrVp is a multidrug-resistant human breast cancer subline that displays an ATP-dependent reduction in the intracellular accumulation of anthracycline anticancer drugs in the absence of overexpression of known multidrug resistance transporters such as P glycoprotein or the multidrug resistance protein. RNA fingerprinting led to the identification of a 2.4-kb mRNA that is overexpressed in MCF-7/AdrVp cells relative to parental MCF-7 cells. The mRNA encodes a 663-aa member of the ATP-binding cassette superfamily of transporters that we term breast cancer resistance protein (BCRP). Enforced expression of the full-length BCRP cDNA in MCF-7 breast cancer cells confers resistance to mitoxantrone, doxorubicin, and daunorubicin, reduces daunorubicin accumulation and retention, and causes an ATP-dependent enhancement of the efflux of rhodamine 123 in the cloned transfected cells. BCRP is a xenobiotic transporter that appears to play a major role in the multidrug resistance phenotype of MCF-7/AdrVp human breast cancer cells.

https://www.pnas.org/content/pnas/95/26/15665.full.pdf

A multidrug resistance transporter from human MCF-7 breast cancer cells

Erratum: A multidrug resistance transporter from human MCF-7 breast cancer cells (Proceedings of the National Academy of Sciences of the USA (December 22, 1998) 95 (15665-15670))

A key assumption in the analysis of microarray data is that the quantified signal intensities are linearly related to the expression levels of the corresponding genes. To test this assumption, we experimentally examined the relationship between signal and expression for the two types of microarrays we most commonly encounter: radioactively labeled cDNAs on nylon membranes and fluorescently labeled cDNAs on glass slides. We uncovered two sources of nonlinearity. The first, which led to discrepancies in analysis affecting the fluorescent signals, was signal quenching associated with excessive dye concentrations. The second, affecting the radioactive signals, was a nonlinear transformation of the raw data introduced by the scanner. Correction for this transformation was made by some, but not all, image-quantification software packages. The second type of nonlinearity is more troublesome, because it could not have been predicted a priori. Both types of nonlinearities were detected by simple dilution series, which we recommend as a quality-control step.

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Sources of nonlinearity in cDNA microarray expression measurements

Microarray experiments involve many steps, including spotting cDNA, extracting RNA, labeling targets, hybridizing, scanning, and analyzing images. Each step introduces variability, confounding our ability to obtain accurate estimates of the biological differences between samples. We ran repeated experiments using high-density cDNA microarray membranes (Research Genetics Human GeneFilters® Microarrays Version I) and 33P-labeled targets. Total RNA was extracted from a Burkitt lymphoma cell line (GA-10). We estimated the components of variation coming from: (1) image analysis, (2) exposure time to PhosphorImager® screens, (3) differences in membranes, (4) reuse of membranes, and (5) differences in targets prepared from two independent RNA extractions. Variation was assessed qualitatively using a clustering algorithm and quantitatively using a version of ANOVA adapted to multivariate microarray data. The largest contribution to variation came from reusing membranes, which contributed 38% of the total variatio...

Identifying and Quantifying Sources of Variation in Microarray Data Using High-Density cDNA Membrane Arrays

Identifying and quantifying sources of variation in high-density cDNA microarray data using 33 P-labeled probes

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Tammy Krogmann

Papers

A multidrug resistance transporter from human MCF-7 breast cancer cells

Erratum: A multidrug resistance transporter from human MCF-7 breast cancer cells (Proceedings of the National Academy of Sciences of the USA (December 22, 1998) 95 (15665-15670))

Sources of nonlinearity in cDNA microarray expression measurements

Identifying and Quantifying Sources of Variation in Microarray Data Using High-Density cDNA Membrane Arrays

Identifying and quantifying sources of variation in high-density cDNA microarray data using 33P-labeled probes