DualChip microarray as a new tool in cancer research

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DualChip® microarray as a new tool in cancer research Jean-Pierre Gillet, Françoise de Longueville and José Remacle†

CONTENTS Molecular markers in breast cancer Resistance to chemotherapy Ongoing diagnostic tools Microarrays as a diagnostic tool DualChip® technology: a low-density DNA microarray Clinical applications of DualChip® technology Expert commentary Five-year view Key issues References Affiliations

Over the last 5 years, the emergence of gene expression profiling using high-density DNA microarrays led to a better understanding of tumor development and identified new prognostic markers. However, high-density microarrays failed to leap from the researcher’s bench to the clinical practice due to their cost, data management and lack of standardization. DualChip® low-density DNA microarrays were developed as a new flexible tool that is able to reliably quantify the expression of a limited number of genes of clinical relevance. This review will illustrate how DualChip technology can be applied to tumor diagnosis and tumor-acquired drug resistance. Expert Rev. Mol. Diagn. 6(3), 295–306 (2006)

Despite the variety of clinical, morphological and molecular parameters used to classify human malignancies, patients diagnosed with similar tumor types can respond differently to identical treatments, resulting in different disease outcomes. The history of cancer diagnosis has been marked by successive evolutions in the classification of tumor subgroups. This review will discuss the many meaningful ways that microarrays can be used in tumor classification and identification of chemotherapy-induced resistance. Two low-density microarrays developed for the purpose of tumor diagnosis and how they offer new perspectives in our understanding of cancers will be discussed. Finally, how these low-density microarrays can be used as a powerful tool in daily clinical practice and in patient treatment will be covered. Molecular markers in breast cancer

†

Author for correspondence Manager, Eppendorf Array Technologies (EAT), 21 Rue du Séminaire, 5000 Namur, Belgium Tel.: +32 81 725 611 Fax: +32 81 725 614 [email protected]

KEYWORDS: ATP-binding cassette transporter gene, breast cancer, DualChip® microarray, leukemia

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In Western countries, one in ten women will develop a breast carcinoma [1,2]. For instance, the most current US statistics demonstrate that the probability for a woman to develop invasive breast cancer is approximately 13.5% [1]. Despite considerable progress in tumor detection as well as in radio-, chemo- and hormone therapy, more than a third of patients still succumb to the disease. In most cases, death results from the dissemination of cancer cells and their proliferation at secondary sites.

10.1586/14737159.6.3.295

To ensure better characterization and efficient treatment of the extensively heterogeneous breast tumors, new approaches are needed to complement the traditional pathological analyses. Cutting-edge tools that exploit the most recent advances in molecular biology and medical imaging can help the clinician to predict the evolution of tumors with a high degree of confidence, including their sites of metastases as well as the response to different therapies. One major obstacle against the generalized use of new technologies in tumor diagnosis is their cost [3]. The highly heterogeneous nature of breast tumors is reflected in the diversity of their transcriptional profiles, metabolic characteristics, ability to proliferate and ability to respond to drugs or hormones. Therefore, it is important to detect tumor markers that are significantly relevant for the clinical treatment of cancers. Thousands of human genes have been cloned and characterized. These studies, along with the human genome sequencing project, provided important insights into how these genes are organized. A reliable clinical marker requires extensive, strictly controlled and reproducible expression studies on a large number of samples. To date, this process has been completed for only a few candidates. For instance, the estrogen receptor (ER)α

© 2006 Future Drugs Ltd

ISSN 1473-7159

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Gillet, de Longueville & Remacle

(gene ESR1), a well-established breast cancer marker, has been used as a molecular landmark for more than 30 years. This has led to its definitive validation as a prognostic marker for patient responsiveness to anti-estrogens [4]. Her2/neu (c-erbB-2) is another marker with prognostic relevance, which has been used to develop antibody-based therapies. It is overexpressed in approximately 25% of breast cancers, as well as in ovary, prostate, lung and gastrointestinal tract adenocarcinomas [5,6]. Trastuzumab is a humanized monoclonal antibody (mAb) derived from the murine mAb4D5 that recognizes an epitope on the extracellular domain of Her2/neu [7,8]. Approximately 15% of women who were treated for metastatic breast cancer overexpressing Her2/neu responded to trastuzumab therapy [5]. Trastuzumab given either alone or in combination with paclitaxel was recently approved by the US FDA for the treatment of women with metastatic breast cancer positive for Her2/neu [5,9,10]. Urokinase-type plasminogen activator (uPA; gene PLAU) and plasminogen activator inhibitors-1 and -2 (SERPINE1 and SERPINB2) are three additional markers that have recently been introduced into clinical practice after extensive investigation of their expression levels in breast tumors. They all possess prognostic and predictive properties; moreover, uPA might be the target of antiprotease-based therapeutic strategies [11]. The clinical relevance of several other markers has been underscored by studies examining variations in their mRNA and/or protein levels in tumors. These include Bcl-2 (gene BCL2), cathepsin D (CTSD), cyclin D1 (CCND1), epidermal growth factor receptor (EGFR), p53 (TP53), progesterone receptor (PgR), pS2 (TFF1) and urokinase receptor (PLAUR) [6,11]. Apart from the few genes and/or proteins that are generally recognized as reliable clinical indicators, the vast majority of them still require extensive investigation before being validated as reliable markers. Resistance to chemotherapy

Many treatments for malignant diseases rely on antineoplastic drugs as opposed to other therapies, such as radiotherapies, which do not use chemical compounds. Cancer cells have developed several mechanisms to survive exposure to cytostatic drugs [12]. Among them, multidrug resistance (MDR) mediated by ATP-binding cassette (ABC) transporters is a major factor in the failure of many chemotherapies [13,14]. To date, 49 different genes encoding ABC transporters have been identified in the human genome, which are divided into seven different families based on sequence similarities [101]. A total of 11 ABC transporters are clearly involved in drug resistance [15]. The majority of the experimental and clinical studies relative to drug-mediated resistance were performed using leukemia as a model disease. Many research efforts were focused on the clinical expression of ABCB1 and ABCC1 [16,17]. In adult acute myeloid leukemia (AML), ABCB1 expression has been identified as an independent prognostic factor for induction therapy outcome [16,18–20], whereas ABCC1 expression was an independent marker for disease-free survival [19]. In pediatric AMLs, ABCB1

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expression is not indicative of a poor prognosis [21,22]. Subsequently, other ABC transporters, such as the ABCC (multidrug resistance-associated protein [MRP]) family members ABCC2, ABCC3, ABCC5, ABCC6 and ABCG2, have been shown to be heterogeneously expressed in AML patient cells [20,23]. It has been recently reported that the expression of ABCG2 and ABCC3 is associated with a poor response to chemotherapy in both children and adult AML patients [23–26]. In adult acute lymphoblastic leukemia (ALL), high expression of ABCB1 has been associated with poor clinical outcome [27–29]. Whether ABCG2 plays a significant role on prognosis is currently unknown [30]. Recently, Plasschaert and colleagues investigated the expression level of ABCCs (ABCC1–6) and studied their effect on prognosis. The study demonstrated that a subset of ALL patients with high MRP expression had an age-independent unfavorable prognosis [31]. Resistance to chemotherapy is also a key issue in the management of solid tumors. In breast cancer, most attention has been directed to the clinical role of ABCB1 and ABCC1 [32]. Initial studies highlighted an association between ABCB1 expression and MDR [33,34]. Subsequent studies indicated that ABCB1 expression prior to treatment may be of prognostic value [35–37]; however, other studies reported conflicting results [38,39]. Similarly to ABCB1, numerous studies suggested that absence of ABCC1 expression is indicative of an excellent prognosis for patients with breast carcinomas [40,41], whereas other authors found no or only a weak association of ABCC1 with clinical drug resistance and survival [32,35]. No evidence has been found to date for a clinical role played by ABCC2 and ABCC3 [35,42]. Likewise, the relevance of ABCG2 expression for clinical drug resistance remains unclear. Indeed, Faneyte and coworkers found no indication that elevated ABCG2 expression in breast cancers confers resistance to drug treatment, while Burger and coworkers stated that ABCG2 might have some predictive value for clinical outcome [35,43]. The different results reporting ABC transporter expression in different tumor types demonstrate many discrepancies, making it difficult to decipher the clinical significance of the MDR phenotypes. The absence of consensus methodologies for MDR detection largely contributes to this lack of clarity. Furthermore, it should be noted that these observations result from the expression of other nonclassical MDR transporters, such as ABCA3 [44–47]. In summary, preliminary analysis of ABC transporter expression in tumors was restricted to ABCB1, the ABCC family and ABCG2. To date, only 11 ABC transporters have been implicated in MDR [15]. Further analyses are needed to functionally characterize other and novel ABC transporters. Ongoing diagnostic tools Multidrug resistance mediated by ABC transporter genes

Various techniques have been developed to diagnose ABC transporters at mRNA and protein levels [15]. Quantitative realtime reverse transcriptase (RT)-PCR is a fast and sensitive detection method that enables reproducible quantification of

Expert Rev. Mol. Diagn. 6(3), (2006)

DualChip® microarray

very low amounts of total RNA. Three approaches have been described to quantify 47 human ABC transporters [48–50]. Langmann and colleagues developed a quantitative (q)RT-PCR based on TaqMan® technology using specific primers and only 50 ng of total RNA as template [49]. This group has recently developed an assay that enables the quantification of all 47 human ABC transporters. The platform, Human ATPBinding Cassette Transporter TaqMan Low-Density Array, uses an Applied Biosystems 7900HT Micro Fluidic Card [48]. Rather than 50 ng of total RNA, this assay requires only 2 ng of total RNA. Szakacs and coworkers analyzed the expression level of the same genes with a qRT-PCR method using SYBR® Green and the LightCycler® instrument and 150 ng of total RNA [50]. Four microarray-based detection of ABC transporter genes were recently developed [51–54]. Annereau and colleagues developed a high-density microarray platform, ABC-ToxChip, which contains 2200 cDNA probes complemented with probes specifically matching 36 ABC transporters, as well as 70-mer oligonucleotides allowing the analysis of 18,000 unique human genes [51]. They used 25 µg of total RNA for reverse transcription. Huang and coworkers developed a 70-mer oligonucleotide microarray consisting of 632 probes, covering 40 of the 49 ABC transporter genes among other targets [53]. They used 12.5 µg of total RNA for cDNA synthesis [53]. Gillet and coworkers developed a low-density DNA microarray termed DualChip® Human ABC for the profiling of 38 ABC transporter genes’ expression [52]. A total of 500 ng of mRNA or 10 µg of total RNA is sufficient for reverse transcription. Finally, Liu and colleagues designed a semiquantitative assay to detect the expression of 47 ABC transporter genes [54]. Briefly, this approach consists of an oligo-dT-based reverse transcription of total RNA. The resulting cDNAs are semiquantified using GAPDH levels as references. Normalized levels of cDNAs are used for PCR amplification. The PCR products are then separated by electrophoresis and the intensity of each band is quantified using GelPicAnalyzer software (GeneCopoeia, Inc.). This assay requires 4 µg of total RNA. This approach has two main drawbacks: it is not reliable for detection of moderate changes in expression levels, and it is not applicable for quantitative detection of abundant mRNAs [54]. Microarrays as a diagnostic tool

In diseases such as cancer, many biological pathways and cell functions are irreversibly altered at the transcriptional, translational and protein level. Various technologies exist in order to investigate cancer-related modifications in the cells. One promising approach relies on microarray expression profiling. Microarrays enable a precise analysis of multiple parameters in a miniaturized format. The most common microarrays are gene expression arrays, but others, such as genomic, proteomic and tissue arrays, are currently under development and will soon open new perspectives for expression profiling. Standard high-density microarrays use oligonucleotides as probes and require cDNA amplification for hybridization. Each gene requires several capture probes to accurately deter-


mine its expression profile. cDNA microarrays contain long DNA strand-capture probes, which enable highly specific hybridization and render the use of multiple capture probes unnecessary. As a result, analysis and data processing are straightforward. The authors recently developed a panel of low-density microarrays termed DualChip, which is suitable for routine applications due to its simplicity, flexibility, good reproducibility, easy data management and lower costs. The DualChip Xmer™ technology uses single gene-specific polynucleotide capture probes designed to avoid possible crosshybridization. Gene expression microarrays have not been validated as diagnostic tool to date; however, they constitute a clinical research avenue under intense scrutiny. DualChip® technology: a low-density DNA microarray

The DualChip human breast cancer and the DualChip Human ABC microarrays are part of a series of chips developed using a common technology. The DualChip together with their target gene list are presented at [102]. The DualChip microarrays contain two arrays per slide with a range of 150 to 360 triplicate probes depending on each application [102]. These DualChip microarrays were developed to measure differential gene expression between a test and a reference sample [55]. This array is composed of single-strand DNA probes attached to the glass support by a covalent link. Each DNA probe is present in triplicates in addition to a set of control probes. The control probes include positive hybridization controls, negative hybridization controls, positive detection concentration curve, negative detection controls, internal standards (ISs) and a set of housekeeping genes for normalization (TABLES 1 & 2). The gene list together with gene functions and array design are provided at [102]. Accuracy

One of the main challenges for DNA microarray development is to ensure the quality of each probe in terms of specificity and sensitivity. Long probes provide good sensitivity but lack specificity. On the other hand, cross-hybridization can be observed with 25-mer oligonucleotides due to their short length. Eppendorf AG (Hamburg, Germany) has developed a novel approach for probe design. Each DualChip microarray gene is represented by proprietary Xmer probes. These long, singlestranded DNA molecules (200–400 bp) combine the excellent hybridization efficiency of a cDNA fragment with the ability of an oligonucleotide to distinguish between homologous transcripts (FIGURE 1). To optimize the capture probe design for specificity and sensitivity, the length as well as the position within the gene sequence is tested in silico and in situ. Gene databases are checked for the presence of known and predicted splice variants. In some cases, isoform-specific Xmers were designed to discriminate between these splice variants; however, the vast majority of the probe sequences were chosen in consensus regions. Distances to the 3´-poly(A) tail are optimized in order to guarantee that reverse transcription will generate cDNA fragments that overlap the probe sequence. Following the in silico analysis, an in situ approach is used to validate the

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Table 1. Controls present on the DualChip® and their purpose. Control name

Molecule/step

Use

Internal standard

In vitro transcribed polyadenylated

Reverse transcription control, hybridization control and normalization

Positive hybridization control

RNA spiked into reverse transcriptase

Hybridization control, grid alignment

Negative hybridization control

Biotinylated DNA spiked into the hybridization mix

Detection of nonspecific hybridization (subtracted from signal)

Negative detection control

Plant DNA on DualChip

Check of quality of detection

Positive detection concentration curve

Buffer on DualChip

Detection control and for grid alignment (indirect labeling)

Housekeeping genes

Biotinylated capture probe on DualChip DNA

Normalization

Note: Control probes include positive hybridization controls, negative hybridization controls, positive detection concentration curve, negative detection controls, a mix of spiking RNAs (internal standard) and a set of housekeeping genes.

probe design. To confirm Xmer probe specificity and hybridization efficiency, the authors synthesize the corresponding labeled antisense strands, which are hybridized against the entire set of probes. Each Xmer probe demonstrates similar hybridization kinetics to guarantee best hybridization efficiency over the entire set of probes. The combination of these two approaches ensures that the highest design quality for the Xmer probes is reached. Sample labeling

cDNAs used as bait can only be detected on the microarray support if labeled appropriately. The labeling method first involves biotin incorporation during the reverse transcription step in order to obtain a biotinylated cDNA. The biotinylated cDNA is then recognized by cyanine (Cy)3 or Cy5 fluorescently tagged antibiotin antibodies. Subsequently, Cy3 and Cy5 detection is performed with a fluorescent scanner. Alternatively, the authors propose a new colorimetric method based on the used of goldlabeled antibiotin antibodies. The signal is then amplified by a silver enhancement method using the Silverquant® detection kit (Eppendorf ). The amplification is so intense that the spots can be seen by visual inspection. The DualChip is then scanned by the Silverquant scanner (Eppendorf ).

DualChip® data analysis

Hybridized microarrays are visualized with a scanner either by fluorescence or by colorimetric image acquisition. A multiplicity of software is available for image quantification. A spot mask (grid) is adjusted on the image for precise definition of the spot location and the fluorescence intensities are then calculated for each spot. During this step, the classification of pixels as fore- or background signal is performed using dedicated quantification software. It is essential in microarray image analysis to adjust for background signals. Two different kinds of backgrounds must be subtracted from the signal. These subtractions are performed automatically by the DualChip evaluation software. First, the local background intensity (fluorescence emitted from other chemicals on the glass) corresponding to a small region surrounding each masked spot is estimated. The mean of background pixel values is subtracted from the total signal intensity of each spot. Additionally, nonspecific binding signals (e.g., the mean signal of negative hybridization control spots) are subtracted from the signal of every spot. The dynamic range of microarrays is maximized by scanning the same arrays with three different photomultiplier tube (PMT) settings. As a result, the number of quantifiable genes is

Table 2. Summary of the controls that enable the validation of each step of the analytical process. Positive detection control*

Internal standards

Positive hybridization controls‡

Interpretation

+

+

+

All steps are successful

+

-

-

cDNA synthesis or hybridization step has failed or key reagents are omitted

+

-

+

cDNA synthesis had not occured or key reagents are omitted

-

-

-

Detection step has failed or key reagents are omitted

*Only valid for indirect labeling protocol. ‡ For direct labeling only with the optional hybridization control. +: Positive signal; –: Negative signal.

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Intensity

Efficiency

increased. To do this, an intermediate PMT setting is first chosen to analyze Xmer™ probes Short oligos moderately expressed genes. Highly cDNAs/PCR products Longer oligos expressed genes are accurately quantified at a low PMT setting and low-expressed genes at a high PMT setting. The fluoresHybidization efficiency cence intensities of the whole microarray and sensitivity are typically stored as 16-bit images (Tiff file) and described as raw data. Capture probes are present in triplicates on each DualChip. The triplicate results Homologous gene provide valuable quality information. discrimination On the one hand, it enables the performance of statistical analysis and the correction of the gene data within a single exper20 100 200 400 1000 iment. On the other hand, it allows Length (bp) confidence in the clinical diagnostic. Any of the triplicate outliers are removed for Figure 1. Relation between hybridization efficiency and gene discrimination relative to the probe length. The green curve and the blue curve represent the discrimination efficiency and the hybridization the data analysis. Such outliers can be pro- efficiency relative to the capture-probe length, repectively. duced either by bubbles formed during the incubation or due to dusts present in the sample. The average replicate values (at least two replicates) are the evaluation of the statistical significance of gene expression then computerized and the intensity of each spot is analyzed. levels. The aim of this step is to determine whether gene Spots showing fluorescence intensities that are not in the linear expression levels differ significantly between test and reference range of measurement (e.g., either saturated or very low signal samples or whether the differences are due to random variaintensities) are excluded from quantification. Saturation has a tion. This model assumes that intensities are distributed compressing effect on the ratio, and extremely low signal inten- according a Gaussian distribution. Based on these assumptions, sities yield higher variability in results. All genes that are not formulae were developed for the coefficient of variation and declared as saturated or marginal can be considered quantifiable confidence intervals. The coefficient of variation is estimated for reliable ratio data generation. based on the subset of the ratios of housekeeping genes selected The gene expression ratios consist of the signal intensities of for normalization, which are assumed to be stable between test the test sample compared with the reference. If the signal inten- and reference samples. This process is based on the assumption sities for both the test and the reference are quantifiable, a ratio that the variation of housekeeping gene ratios is representative is termed quantitative. If only one intensity is quantifiable (test for the variation of nonregulated genes. Therefore, the selected or reference), the ratio is said to be qualitative. If neither inten- housekeeping genes should have ratios distributed around a sity is quantifiable, the ratio is designated as not being in linear value of 1. range (FIGURE 2). The authors performed a normalization of the results based on two steps. DualChip microarrays are divided into six zones, each containing two different densities of IS capture probes. To ensure the acceptable expression of at least one IS per zone, two different concentrations of IS capture probes were spotted (low and high concentration). This design enables the computing of Quantitative a local normalization factor for each zone using the quantifiable IS signal intensities. This local normalization factor is calQualitative culated from the intensity ratios of the ISs of the corresponding Not in linear range zone in the reference and test samples. The ratios for each gene between test and reference samples in that local zone are corFigure 2. Graphical display of expression data defined as quantitative, rected by the computed local normalization factor. ISs (exogequalitative, or not within the linear range. Relative gene expression is nous RNA spikes) do not reflect the purity, quality or amount calculated as the ratio between the test sample and the control sample. If the signal intensity for both the test and the control are within the linear of the analyzed RNA. Therefore, a second normalization step, range, the ratio is termed ‘quantitative’. If only one intensity is included based on the expression levels of housekeeping genes, is neceswithin the linear range of the curve (test or reference), the ratio is said to be sary. A housekeeping normalization factor is calculated using ‘qualitative’. If both intensities are outliers, the ratio is termed ‘not within the the mean of the housekeeping gene ratios, which are quantifialinear range’. ble. The DualChip evaluation software uses a statistical test for


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The significance of the ratios is established using the confidence interval computed from the statistical model: ratios outside the 95% confidence interval are considered as statistically significant, whereas ratios outside the 99% confidence interval are accepted as highly significant. Furthermore, the ratios of analyzed genes (e.g., coming from test divided by reference) are grouped into (semi)quantitative and qualitative. Ratios between two quantifiable signal intensities are considered to be quantitative. Ratios between one low and/or one saturated intensity are considered as qualitative. Ratios resulting from two low or two saturated intensities are designated as not in linear range (FIGURE 2). Experimental methodology Reference sample

One major element to consider for microarray-based analyses involves the choice of an appropriate reference. The selection of a proper reference is often under debate; ideally the reference sample should be readily available, in sufficient amount, homogeneous and stable over time. However, a few authors have argued that the use of reference samples is not necessary and that the comparison with a reference sample is not always meaningful [56,57]. Some references have been constructed using complex RNA mixtures from tissues or cell lines in an attempt to light-up every spot on an array. Another strategy consists of pooling the references to obtain a representative mRNA population. [58]. In two recent studies, a theoretical reference has been computed by combining multiple expression datasets representing a variety of tumors. This reference was used as a standard to define overexpressed or downregulated genes in tumors [EFFERT T, UNPUBLISHED,59]. Test sample (tumor), material amount & handling

One critical element in sample handling is to minimize RNA instability; this requires preserving tumor biopsies in liquid nitrogen prior to RNA extraction [103]. For solid tumors, the authors recommend the use of six 50 µm (0.5 cm2)-thick frozen sections, which should contain enough RNA material to perform hybridizations in triplicates with no amplification steps. Total RNA extraction is performed by the Trizol method [104] or using commercially available kits. RNA integrity is a critical factor to avoid false negatives in the hybridization process and, therefore, it systematically checked by the Bioanalyzer (Agilent Technologies, Inc.) [105]. The amount of RNA to be used in the assays is dependent on the transcriptional activity of the tissue and will determine whether an amplification step is necessary. The authors advise that a concentration curve should first be performed with a minimum of 1 µg to a maximum 20 µg of total RNA. According to the authors’ experience, 5–10 µg of total RNA extracted from tumor biopsies is generally required for the prehybridization procedure. One amplification step using the T7 polymerase can increase the sensitivity by fivefold.

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According to the proposed protocol, an experiment can be performed within 2 days. On day 1, the researcher extracts and verifies a RNA sample’s integrity, then performs the RT and starts an overnight hybridization. On day 2, after successive washing steps, scanning and quantification reveal the gene expression profile which is then analyzed using the Eppendorf DualChip analysis software. Validation of microarrays

Quantitative RT-PCR is the gold standard technology used to validate microarray results. In Gillet and coworkers’ study, results for ABC transporter genes with an expression ratio greater than 2 were confirmed by qRT-PCR. Expression of three different ABC genes, namely ABCA7, ABCB1/4 and ABCF2, was quantified in the parental drug-sensitive CCRFCEM cell line and in the drug-resistant CCRF/ADR5000 subline. According to the microarray results, ABCB1/4 was induced 100-fold in the drug-resistant cell line compared with the control, while real-time RT-PCR gave a ratio of 799. Both results confirmed a strong overexpression of ABCB1/4 in drug-resistant cells. Microarray and qRT-PCR results where highly consistent for ABCA7, with a 2.15- and 2.7-fold increase, respectively [52]. The authors also reported similar results using two breast tumor samples. In this study, 15 ABC transporter genes were selected and their expression was investigated using both microarray analysis and real-time RT-PCR. qRT-PCR expression values correlated significantly with the corresponding values obtained by microarray analysis (p = 9.27 × 10-6, correlation coefficient [r] = 0.716; Pearson’s correlation test) [59]. In Efferth and coworkers’ study, seven ABC transporter genes were selected and their expression was quantified both by qRT-PCR and microarray in four ALL samples (three poor responding patients used as test compared with good responding patients as reference). All seven ABC transporter expression profiles were similar from microarray to qRT-PCR (p = 1.53 × 10-7, r = 0,935; Pearson’s correlation test) [EFFERT T, UNPUBLISHED]. In Steinbach and coworkers’ study, four selected genes were analyzed in 42 patients as well as in 18 healthy bone marrow samples. The microarray data were confirmed with the qRT-PCR results [STEINBACH D, UNPUBLISHED]. Other studies have also reported valid correlations between real-time PCR and low-density microarray data, indicating that the DualChip is a quantitative and reproducible tool [55,60–62]. DualChip® microarray: scientific relevance, advantages & pitfalls

While high-density DNA microarrays are useful to highlight new interesting markers, the DualChip is a targeted tool designed for diagnosis and for monitoring expression profiles in clinical biopsies. The assay is quantitative, reliable, sensitive and reproducible. Due to the limited number of capture probes, the data-processing steps are simplified and the results are readily available (quantitative and qualitative



ratios, nonstatistically significant value, and nondetected genes). Moreover, the A B microarray and real-time PCR expression profiles are consistent, indicating that the DualChip is quantitative. The DualChip technology generally requires between 5 and 10 µg of total RNA. However, when using cells with low transcriptional activity (e.g., small B-cell non-Hodgkin lymphoma) this amount should be increased to up to 10 µg or even 25 µg of total RNA. Alternatively, an amplification step might be introduced to Fluorescent intesity level reduce the initial amount of template RNA that is required. However, the Maximum Minimum amplification is time consuming and can alter the relative representation of each Figure 3. Breast cancer microarray hybridized with (A) cDNA derived from IBEP-3 breast cancer gene since the amplification efficiency cells and (B) a pool of mRNAs isolated from the 11 different breast cancer cell lines. Fluorescence vary from one mRNA to the other. intensities are represented in pseudocolor scale and correspond to the gene expression levels. Laser power The DualChip microarrays are low- of 100% and photomultiplier tube gain is 70. density arrays containing a maximum of 360 different probes focusing on specific application rather Clustering analysis of expression data enabled these cells to be than covering the whole genome. High- and low-density sorted into two categories and, in line with other studies, suparrays have been developed to respond to distinct needs. High- ported a major discriminatory role for ERα (FIGURE 4). The three density microarrays are usually used for experimental screen- ERα-positive cell lines (BT-474, MCF-7 and T-47D) and the ing with no a priori, while low-density arrays were developed three ERα-negative lines (Hs578T, MDA-MB-231 and to study a subset of genes relevant to one specific research area MDA-MB-453) were clearly grouped in two distinct categories, or pathology. confirming that the expression status of many genes is correlated (positively or negatively) in BCC lines, with the expression Clinical applications of DualChip® technology of ESR1 [63]. DualChip® human breast cancer To date, the second set of BCC lines (Evsa-T, IBEP-1, The DualChip human breast cancer contains a total of 210 genes IBEP-2, IBEP-3 and KPL-1) has not been well characterized. divided in two categories, one category for tumor classification The authors seized this opportunity to use the DualChip sysaccording to the cell origin, and the second category associated tem to define an expression profile-based molecular signature for each cell line. Two cell lines out of five were previously with the hereditary types of BRCA1 and BRCA2. They also contain genes involved in different cell functions, found to be ERα positive, namely KPL-1 and IBEP-2 [64]. As such as angiogenesis, apoptosis, cell adhesion, cell cycle, cell expected, they were identified as ERα positive. The ERα-negadifferentiation, cell signaling, cell structure, defense system, tive Evsa-T cells were classified among the well-characterized DNA repair, extracellular matrix, growth factors and cytokines, ERα-negative lines (Hs578T, MDA-MB-231 and MDA-MBmetabolism, oncogenesis, transcription, and tumor suppressors. 453) [67]. Data obtained for IBEP-1 and -3 revealed that they These microarrays provide an overview of many fundamental expressed ESR1 mRNA, contrasting with its absence in other oncogenic processes, such as cell cycle regulation and cell response ERα-negative cells (Hs578T, MDA-MB-231 and MDA-MBto immune signals. They are also informative on how cells adapt 453). Moreover, both IBEP-1 and -3 express a subset of PgRs, in response to various clinical and experimental situations. which are frequently observed in ERα-positive cells and might Several studies have already taken advantage of the DualChip be induced in an ERα-dependent fashion. Interestingly, technology and are summarized hereunder. First, the arrays were IBEP-1 cells were isolated from a patient with a well-differentivalidated on isolated cell lines (BCC) in order to verify that they ated ERα-positive tumor, further supporting that these cells can be used to specify the cell type and that they can discriminate were initially misclassified as ERα-negative cells [68]. The between ERα-positive or -negative cells [63]. FIGURE 3 illustrates an authors have now firmly established that IBEP-1 and -3 BCC example of data obtained with an mRNA from IBEP-3 cells lines are ERα positive and express receptors at both mRNA and protein levels similar to those observed in MCF-7 and IBEP-2 (FIGURE 3A) and a pool of mRNAs isolated from the 11 different BCC. These observations underline the central place occupied breast cancer cell lines used as reference (FIGURE 3B). The first six cell lines (BT-474, Hs578T, MCF-7, MDA-MB- by ERα in defining the luminal epithelial-like phenotype. They 231, MDA-MB-453 and T-47D) have been extensively charac- also demonstrate that low-density microarrays are powerful terized, notably by high-density microarray analysis [64–66]. tools that can challenge the pre-existing tumor classification.


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ERα+ ERα41.532 27.688 13.844 0

A

B

The authors’ experiments also confirmed the overexpression of ERBB2 in BT-474 and, to a lesser extent, in MDA-MB-453. In both cell lines, the ERBB2 gene has been duplicated through chromosomal rearrangement, resulting in higher ERBB2 mRNA abundance [69,70]. For the first time, the authors detected mRNA and protein enrichment for PIP in MDA-MB-453. This phenomenon is cell specific as it is not observed in other BCC lines. PIP, also known as gross cystic disease fluid protein-15, is a 14-kDa protein that is considered a highly specific and sensitive marker for apocrine differentiation [71]. This marker has been used to detect and follow the progression of breast cancer. PIP is detectable in most breast tumor biopsies. Studies in cell lines also revealed that, in addition to prolactin, various hormones and cytokines regulate PIP expression. Estrogen decreases PIP expression, while it is maximally upregulated by androgens [72]. Androgens are also known to increase prolactin receptor levels in MDA-MB-453 [73]. MDA-MB-453 are relatively rich in prolactin receptors, they express neither ERα nor PgRs, but possess a high amount of androgen receptors, which is a general characteristic of apocrine breast cells [74]. The authors suggest that MDA-MB-453 BCC can be used as a pertinent model for apocrine breast cancer cells. DualChip® Human ABC

Figure 4. Hierarchical clustering analysis representing relative gene expression between cell lines. (A) Complete cluster diagram including 145 transcripts across 11 independent BCC lines. The red–green pseudocolor chart depicts individual gene expression data; red blocks depict overexpressed genes, whereas green blocks depict underexpressed genes. The dendrogram groups cell lines with similar expression patterns. (B) Representation of transcripts that are expressed at a high level in only one of the studied cell lines. ER: Estrogen receptor.

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The DualChip ABC microarray contains probes for 38 ABC transporters and three other transporters (Kir 6.1, Kir 6.2 and IMPT) [52]. As a consequence of the high homologies between ABC transporters, five capture probes are complementary to multiple genes (ABCA2/3, ABCB1/4, ABC6/8/9 and Kir 6.1/6.2). The DualChip ABC was validated using various approaches. The first validation was performed on three multidrug resistant sublines known to express three different ABC transporter genes, namely ABCB1, ABCC1 and ABCG2 [52]. The parental drug-sensitive cell lines were used as a reference and their corresponding drug-resistant counterparts as a test. As expected, the authors were able to detect the overexpression of ABCB1, ABCC1 and ABCG2 in the drug-resistant cell lines. Besides the expected overexpression of specific ABC transporter genes, the authors detected the overexpression of additional ABC genes that had not been reported before. This was unexpected; to explore which ABC transporters might function as drug transporters, the authors performed a COMPARE analysis. This study was performed with compounds included in the National Cancer Institute’s Standard Agent database [106] and 31 ABC transporters whose mRNA expression in 60 National Cancer Institute cell lines has been determined by microarrays [73]. The study indicated that 17 of the 31 ABC transporters investigated may act as drug transporters. The results of the COMPARE analysis reinforce the use of the DualChip Human ABC as a tool to detect ABC transporter-associated drug resistance [52]. The DualChip was then tested on clinical samples of pediatric AML and pediatric T-ALL [EFFERT T, UNPUBLISHED, STEINBACH D, UNPUBLISHED]. As aforementioned, a major issue in the treatment



of AML and T-ALL is resistance to chemotherapeutic drugs. The aim of the studies was to help identify genes that cause drug resistance and could be used as therapeutic targets. The microarray results enabled the identification of ABCA2, ABCA3, ABCB2 and ABCC10 as ABC transporters that are overexpressed in AML samples when compared with two reference samples of healthy bone marrow. This finding was verified by real-time PCR in a larger group of patients and using a larger group of healthy controls. Out of the four new ABC transporters that were found to be overexpressed in pediatric AML, only ABCA3 was associated with a poor response to therapy. Therefore, the authors propose that ABCA3 is the likely candidate involved in AML drug resistance [STEINBACH D, UNPUBLISHED]. T-ALL samples with good and poor response to chemotherapy were then screened. Beside the four ABC transporters, ABCA7, ABCB2, ABCC1 and ABCF1, that were highly expressed, the analysis demonstrated that ABCA2/A3 was also overexpressed in most analyzed ALL. This result is indicative that both ABCA2 and ABCA3 are of importance in ALL MDR. Their involvement in this mechanism was also suggested by the increased sensitivity of cells transfected with ABCA2 and ABCA3 small interfering RNAs [EFFERT T, UNPUBLISHED]. Finally, the DualChip Human ABC was used to analyze the expression of ABC transporters in breast tumor biopsies [59]. The majority of all 38 ABC transporters were detected in tumors cells from treated and untreated patients: 23 genes out of the 41 present on the array were significantly expressed in a majority of tumors. This is a rather unexpected result that indicates that these transporters are present in untreated samples and can be potentially further overexpressed following drug treatment. However, the authors were not able to establish a direct comparison between treated and untreated patients due to the limited supply of biopsy samples, and ABC transporter expression appeared very similar in both groups. Out of the 23 genes detected, ABCA2/3, ABCC1 and ABCC5, which are known to be involved in the transport of chemotherapeutic drugs, were highly expressed in treated and untreated tumors. Only one gene, ABCC6/8/9, was higher in the treated group when compared with the untreated samples. Again, additional biopsies will be required in order to determine whether this result is statistically significant [59]. In summary, the authors developed a quantitative and reliable assay to detect ABC transporter expression. The authors demonstrated that the DualChip Human ABC microarray can contribute to our understanding of drug resistance and how tumor cells adapt to drug treatment. Finally, this microarray opens new avenues for the diagnosis of MDR in clinical samples and for evaluating ABC transporter expression profiles in clinical biopsies. Expert commentary

All the cited studies illustrate the potential use of the DualChip microarray system for clinical research in hematology [EFFERT T, UNPUBLISHED, STEINBACH D, UNPUBLISHED] and in solid


tumors [59,63]. The results demonstrated that the DualChip tool gives quantitative, reliable and reproducible data [52,55]. To date, the DualChip system has been used for research purposes, and now the challenge is to introduce this technology as a standard clinical test. The benefit of the DualChip has to be evaluated by clinicians in terms of cost, workload and gene content. A close collaboration between clinicians and molecular biologists is required to expand the use of the DualChip microarray in routine clinical diagnostics. Five-year view

Microarray-based studies gave significant insights into how tumor cells can bypass the cell cycle checkpoints. Expression profiling can be used as a quantitative and qualitative integrator of multiple oncogenic parameters. Low-density DNA arrays, such as the DualChip microarrays, can play a central role in tumor characterization and can ideally complement the conventional diagnostic methods. Over the next 5 years, it is expected that molecular diagnostics methods will provide specific information regarding the biology of individual tumor sample. Therapies will be adjusted accordingly in order to achieve successful treatment and minimum side effects. Gene expression microarrays, and for instance the DualChip microarray, are tools developed to tackle clinical problems such as tumor drug resistance. Several other methods, including chip technology, are being developed to examine protein expression in cells. Among these assays, are the cytokine and transcription factor chips. To further understand tumor development, integrative methods (computer science, molecular biology and profiling, and pathobiology) that define networks of interaction at the DNA, protein and metabolic level, must be used. This multidisciplinary approach is often referred to as systems biology [76]. Recent publications reported the development of data integration methodologies that can handle multiple data sets differing in type, size and network coverage [77,78]. As a consequence, the description of a given tumor will be increasingly accurate, thus providing new strategies for patient treatment. Acknowledgements

The authors thank Emmanuel Minet and Thierry Arnould for critically reviewing this manuscript. Key issues • Recent studies revealed new prognostic and diagnostic markers in many cancers. • High cost due to the number of markers to screen. • Diagnostic tools require standardized methods. • Due to their cost, data management and lack of standardization, high-density microarrays failed to leap from the researcher’s bench to the clinical practice.

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Affiliations •

Jean-Pierre Gillet, PhD Researcher, University of Namur, URBC, 61 Rue de Bruxelles, 5000 Namur, Belgium Tel.: +32 81 725 711 Fax: +32 81 724 135 [email protected]

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Françoise de Longueville, PhD Team Manager, Eppendorf Array Technologies (EAT), 21 Rue du Séminaire, 5000 Namur, Belgium Tel.: +32 81 725 615 Fax: +32 81 725 614 [email protected]

•

José Remacle, PhD Manager, Eppendorf Array Technologies (EAT), 21 Rue du Séminaire, 5000 Namur, Belgium Tel.: +32 81 725 611 Fax: +32 81 725 614 [email protected]
