Array of Informatics: Applications in Modern Research Anna Kozarova, Steven Petrinac, Adnan Ali, and John W. Hudson* Department of Biological Sciences, University of Windsor, 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada Received November 30, 2005
The advent of microarray technology in the past decade has greatly enhanced gene expression studies and allowed for the acquisition of a vast amount of information simultaneously. Microarrays have been used in numerous scientific fields to identify new genes, to determine the transcriptional activity of cells, and to discover downstream targets of different loci. Recently, DNA microarrays have also been utilized in disease studies to determine outcomes at many levels including diagnosis, prognosis, and drug therapy. The promise of protein microarrays is to allow us to study the molecular interactions of protein, lipids, small molecules, and carbohydrates. They can be exploited to analyze a single protein pair interaction, to address changes in multiple protein levels as a response to treatment (i.e., drug or radiation), or in a pathological condition. Tissue microarrays allow the analysis of numerous tumor samples simultaneously. Finally, live cell-based microarrays provide an opportunity to study the function of the entire proteome en masse within living cells. However, these exciting new areas still have to overcome many inherent problems. In this review, we discuss novel microarray-based approaches that are in development and that have potential in applications for medicine, biotechnology, and basic research. Keywords: Molecular Biology • Gene expression • Genome • Microarrays • Disease • Therapeutics
Introduction Over the past few years, innovations in large-scale DNA sequencing have led to an explosion of nucleotide sequence information in higher eukaryotes including man,1 mouse,2 chimp,3 and Drosophila.4 Biological research is on the threshold of a revolution. The sudden explosion of nucleotide sequence data has allowed access for nearly every human gene. However, the in-depth exploration of this information will largely depend on the success or failure of novel methods of investigation. In terms of cancer research, integrated parallel analysis at the genome, transcriptome, and proteome levels is necessary to elucidate tumor behavior.5 The complex molecular biology underlying cancer includes alterations at the DNA level, such as mutations, deletions, partial chromosome loss/gains, and rearrangements, as well as changes in methylation patterns. The introduction of microarray technology has allowed us to examine various biological questions on a genome-wide scale, has influenced the way in which gene expression studies are carried out, and has provided a systematic way to study gene expression across entire genomes. Complementary to genomics analysis, is the elucidation of the proteome, allowing one to study changes at the protein level. In this review, we examine some of the novel array-based approaches that are either commonly used in research today or that are in development. Chronologically, in 1995, cDNA arrays were developed to measure the differential expression of genes in correlation to specific mRNA levels.6 Gene expression profiles are generated * Corresponding author. Tel.: +1 (519) 253-3000, ext., 2715. Fax: +1 (519) 971-3609. E-mail:
[email protected]. 10.1021/pr050432e CCC: $33.50
2006 American Chemical Society
as an image of mRNA levels of specific genes, that is, the characterization of the mRNA accumulation in whole cells (Figure 1A). More specifically, mRNA from two different samples are reverse-transcribed into cDNA such that it is fluorescently labeled with either Cy3 (green, sample 2) or Cy5 (red, sample 1) dyes. The labeled cDNAs are simultaneously hybridized to complementary sequences which are attached/ printed on glass slides as high-density discrete spots (oligonucleotides- or cDNA-based arrays). The levels of one fluorophore as compared to the other are then used to determine the relative mRNA levels of specific mRNAs in each sample. DNA microarrays offer an excellent way to explore and profile the gene expression pattern of hundreds of thousands of genes simultaneously. The flexibility of this experimental approach has precipitated the identification of cancer-specific biomarkers,7,8 new genes,9,10 and downstream targets of signaling pathways.11,12 DNA microarrays have also been used to detect single nucleotide polymorphisms and allelic imbalances due to the complete or partial loss of chromosomal DNA. They are based on a combination of oligonucleotide array technology with single nucleotide polymorphism (SNP)-containing loci amplified by multiplex-PCR from genomic DNA.13-16 In many invasive tumors, progressive genomic instability has been correlated to the stage of the disease as the loss of one allele can have tremendous consequences on gene expression and the development/progression of malignancy. A hallmark of cancer is the loss of normal gene expression of key cellular regulators by mutation, silencing by methylation, and haploinsufficiency. Furthermore, DNA microarrays have been modified to allow the study of cancer-induced methylation changes; Journal of Proteome Research 2006, 5, 1051-1059
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Figure 1. DNA-based microarray. (A) The analysis of the transcriptome is performed by measuring the differences at the mRNA level between experimental and control samples. Briefly, RNA is isolated from cells/tissue for each sample and reverse-transcribed with either Cy3 or Cy5 incorporated individually into one sample. Subsequently the samples are combined together and hybridized onto the glass slide, which contains complementary DNA sequences (oligo- or cDNA-based) printed as discrete spots. (B) The analysis at the genomic level is performed by determining the changes in the methylation pattern between two samples. Briefly, genomic DNA is isolated from experimental and control samples. Genomic DNA is then digested with specific restriction endonuclease(s) and subjected to bisulfite treatment, thus, unmethylated cytosines (C) are converted into uracils (U). Next, the modified DNA is PCR-amplified using Cy5-labeled random primers (during which uracils (U) are replaced by thymines (T)). Thereafter, each sample is individually hybridized to oligonucleotide (oligo) arrays that contain sequences corresponding to the predicted methylated genomic region of interest.
the transcriptional inactivation of tumor suppressor genes due to DNA hypermethylation at CpG islands is an early indicator of cancer onset.17 CpG islands are rich in CpG dinucleotides and are usually hypomethylated in active genes. Oligonucleotide arrays have undergone tremendous improvements in their design since 2001, when the first arrays were developed.18,19 For example, Yu and co-workers constructed an 1052
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oligonucleotide-based methylation array to determine the methylation profile in prostate cancer of more than 100 genes and ESTs (Figure 1B).20 To detect CpG island methylation, the genomic DNA from cancer cell lines and patient biopsies was digested with restriction enzymes and subsequently modified by bisulfite treatment. This treatment results in conversion of unmethylated cytosines to uracil, whereas methylated cytosines
Array of Informatics
are protected and thus remain unmodified. The treated DNA was then PCR-amplified using Cy5-labeled random primers to create fluorescent-labeled probes. These probes now contain TG dinucleotide repeats instead of CG dinucleotide repeats wherever the original genomic DNA was unmethylated. The amplified probes were then hybridized to the array to detect the methylation status of numerous genes simultaneously. The array was designed such that it contained 21-25 bp long oligonucleotides corresponding to methylated and unmethylated versions of known CpG island regions. To further decrease cross-hybridization between the methylated and unmethylated DNA, the target sequences were chosen such that the methylated version of the amplified DNA would differ from the unmethylated by at least 3 nucleotides. Twenty-five out of more than 100 genes tested were methylated in all prostate cancer samples, but not in normal tissue samples.20 Numerous clinical studies have used the DNA microarray platform in the context of cancer to study changes in gene expression to identify prognostic biomarkers that are specific to the onset and progression of cancer, for disease classification, responsiveness to drug treatment, for the development of drug resistance, and for the risk of relapse assessment. Overall, DNA microarray-based profiles have provided us with enormous pools of information that have helped us to understand many molecular changes underlying malignancy. Protein Arrays. In contrast to the plethora of genomic and transcriptional information which can be obtained from cDNA/ oligonucleotide micoarrays, one of the major drawbacks is that the technology does not provide information regarding the functions and changes in protein levels. While in the past a direct correlation between mRNA levels and protein levels has often been inferred, increasing evidence indicates differences in expression profiles at the mRNA and the protein levels.21,22 Moreover, when mRNA and protein expression patterns for identical samples were compared, cell-structure-related proteins are more highly correlated with mRNA levels than nonstructure-related proteins.23 The concepts behind protein arrays come directly from DNA microarrays. It was not until the year 2000, 5 years after the announcement of DNA microarray technology, that the first paper describing the protein array format was published.24 The advent of protein array technology has initiated an exciting new phase in cancer research potentially allowing one to screen changes at a level that may have novel implications for cancer biology. They can be used to discover novel protein-protein, protein-small molecule, protein-lipid, and protein-carbohydrate complexes as well as to identify kinase substrates in a high-throughput protein-screening platform. Protein arrays are still in development as they are technically far more difficult to make than nucleotide-based arrays. Proteins are composed of 20 different amino acids; the polypeptide chains are folded into domains which in many cases are subunits of a multimeric complex. Furthermore, a fold is not the only characteristic of a functional protein; post-translational modifications such as phosphorylation, glycosylation, acetylation, methylation, and ubiquitination all have a role in protein stability, activity, epitope availability, as well as the regulation of the protein’s involvement in protein-protein interactions. In addition, proteins are prone to denaturation and degradation due to chemical, physical, and mechanical stress and other small changes in their environment. Simplistically, the overall design of a protein array is very similar to DNA-based arrays. Proteins are attached to a solid
reviews support (comparable to the cDNA clones or oligos in nucleotide-based arrays), and the interacting partner to be detected is applied in solution. Protein arrays are typically made from purified proteins (including highly specific antibodies) that are deposited onto the solid glass support as discrete, spatially separated spots using a contact printer. The proteins may be attached to the support either through covalent bond formation between primary amines and the aldehyde-coated surface of the glass slide24 or through the affinity interaction between a tagged protein such as in the case of His6-tag and nickel-coated surface of the slide.25 In approaches using covalent attachment, the immobilization of the proteins to the solid support results in linkages with different sites on the protein generating a population of randomly oriented molecules which may expose different epitopes for interaction. The randomness inherent in this method can create problems in the consistency and uniformity of signal strength and thus in the subsequent analysis of generated data. Furthermore, known proteinprotein interactions might be destroyed due to preferential binding of modified sensitive/exposed amino acids within specific domains to the solid support, thus, masking the interaction interface with binding partners. In contrast, the affinity interaction method requires the expression of proteins as recombinant His6-tag fusion proteins or as fusions to another suitable epitope tag. The tag allows one to orient the proteins in a uniform manner in the same orientation, thus, generating a more uniform signal. A potential problem is that the epitope tag might artificially enhance some protein-protein interactions leading to an increase in false-positive rates or that it may sterically hinder some interactions resulting in false-negative results. These may partially be resolved through the use of Nversus C-terminal or different epitope tags during the experimental procedure or later during the validation process. The versatility of protein arrays was exemplified by the initial proof of principle experiments described by MacBeath and Schreiber in which they could characterize proteins at the level of protein-protein and protein-small molecule interactions in addition to allowing one to detect substrates for protein kinases.24 In the case of protein-protein interactions, purified proteins were covalently attached to a solid glass support and individually probed with known interacting partners that were modified with a fluorescent moiety (such as the Cy3 and Cy5 fluorophores). Only the expected interactions were detected, and no cross-reactivity in this highly controlled environment was found. An additional experiment by MacBeath and Schreiber tested the feasibility of using protein arrays to identify protein-small molecule interactions. Three unrelated small molecules corresponding to protein ligands were examined in this study. As in the previous example, single proteins were covalently attached to the glass support as discrete spots.24 These were probed with labeled small molecules in solution with a fluorescent signal indicating an interaction. This experimental approach is very promising and provides a high-throughput method that is useful in pharmaceutical industry screens of drug/small molecule lead assays. The ability of kinases to phosphorylate their substrate was also studied in a similar system, where numerous specific substrates of several known kinases were attached to the glass support.24 These substrates were subsequently incubated with one purified kinase at a time in the presence of radioactive ATP. The results of these experiments showed that only the previously identified specific kinase substrates were phosphoJournal of Proteome Research • Vol. 5, No. 5, 2006 1053
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Figure 2. Protein-based microarray. (A) Reverse phase arrays. Whole cell protein lysates are immobilized on a glass support as discrete spots; thus, each spot on the array corresponds to a large number of proteins present in each sample. This allows for the analysis of a huge number of samples at the same time. The protein arrays are then probed with a single antibody at a time to establish protein expression levels. (B) Antibody array. Antibodies are immobilized to the glass support, individually as discrete spots. Proteins for experimental and control samples are labeled with Cy3 and Cy5, respectively, and simultaneously hybridized to the glass slide.
rylated in each case. These initial groundbreaking experiments laid the foundation for many of the novel approaches we are seeing today. Comparative Protein Arrays. Paweletz and co-workers developed reverse-phase protein lysate microarrays in which protein lysates from various samples were deposited robotically onto a solid glass support (Figure 2A).26 These proteins were subsequently probed with specifically labeled antibodies that had been confirmed for specificity. The method was initially limited by the number of protein lysates that could be spotted onto a slide. However, Nishizuka and co-workers improved on the methodology to allow as many as 640 individual lysates to be screened.23,27 They were thus able to identify the diagnostic markers, villin and moesin, that allowed them to discriminate between colon and ovarian adenocarcinoma.23,27 While this type of comparative protein array allows one to study only one protein-antibody complex per slide, it does have some unique and advantageous properties. First, proteins from various cell or tissue samples can be placed side by side in a single array. This may be useful in functional studies where one wishes to 1054
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directly compare specific protein levels in different samples. Second, the sensitivity of this method allows for detection of as few as 2000 molecules per spot. Additionally, the method does not require that the protein lysates be labeled prior to the assay, thus, eliminating the possibility of introducing conformational changes within the protein that could affect binding. Rather, the interaction is detected due to the use of a labeled antibody antigen complex. The method allows a highthroughput analysis of changes that occur in tissues and can be applied to monitor protein levels before and after treatment, for diagnosis and to test for the presence/absence of known or potential markers in diseased tissue. Additionally, with the use of phosphospecific antibodies, one can detect perturbations in the normal phosphorylation/dephosphorylation patterns of many key signaling molecules that are often altered during malignancy. This method does not provide us with any advantages beyond more classical approaches in the identification of novel disease markers. Mass spectrometry-based platforms are better suited for the identification of novel protein biomarkers. However, candidate biomarkers, for which good
Array of Informatics
antibodies are available, can be tested individually, and as such, their usefulness/validity can be ascertained in a high-throughput manner on many patient samples. Antibody Microarrays. Antibody microarrays are designed in the opposite orientation compared to reverse-phase protein arrays. In this case, the proteins attached to the glass slides are specific antibodies (Figure 2B). These antibodies are subsequently used to test for the presence and/or levels of antigen in two different samples. For example, proteins in the experimental sample (possibly subjected to treatment with radiation or a therapeutic agent) are labeled with Cy3 dye. Similarly, proteins in the nontreated control sample are labeled with Cy5 dye. Subsequently, the two samples are mixed together and incubated with numerous specific antibodies presented as discrete spots on a glass slide.28 Using this methodology, Sreekumar et al. were able to identify proteins that were either up-regulated or down-regulated in colon carcinoma cell lines in response to radiation treatment.28 Bartling and co-workers recently adopted antibody arrays to identify putative protein markers for lung cancer.29 Protein samples from biopsies of human squamous cell lung carcinoma and normal adjacent tissue were obtained from patients and subjected to antibody microarrays. Twenty-nine proteins out of 378 were found to have significant changes in their expression profiles when tumor and normal tissues were compared. Antibody microarrays have also been used to detect how drugs and growth factors affect the post-translational modification of specific proteins. For example, Ivanov and co-workers were able to identify changes in the phosphorylation, polyubiquitination, and acetylation of proteins after treatment with growth factors, a proteasome inhibitor, and Trichostatin, respectively.30 Similarly, Kopf and colleagues used a commercial antibody array from Sigma to detect phosphorylated and nonphosphorylated forms of selected proteins.31 Phosphorylation, acetylation, and ubiquitination are all important post-translational modifications of proteins that are often misregulated in malignant tissues. Large-scale detection of differences in the posttranslational modifications observed in normal cells versus tumor cells will be a key step in aiding researchers to further understand the pathways and changes involved in this disease. Antibody microarrays have the potential of allowing the researcher to monitor cell signaling. A proof of principle study by Neilson and co-workers utilized antibody arrays to monitor the abundance of the ErbB tyrosine kinase receptor, its activation, and signaling through the receptor in human cells.32 Results corresponded well to published data but with the advantage of being able to be produced much faster and on a larger scale. These experiments demonstrate the promise of this technology as a way of further characterizing proteins involved in critical cellular functions and may open an array of possibilities for future work in cancer research. Although antibody microarrays offer a promising option for early detection and diagnosis of certain cancers, many problems associated with this technology still need to be overcome. Similar to DNA microarrays, antibody microarrays also exhibit problems associated with variability (variance between duplicate spots within the same slide and between different slides) due to a non-uniform distribution of signal intensity and variation in spot size. Nonspecific binding of proteins may cause additional background noise and affect the sensitivity of the results. In addition, mutated proteins from the tumor tissue may not be detected by the antibodies. That is, deletion mutations, changes in phosphorylation patterns, and/or alter-
reviews ations of the recognized epitope may result in the inability of the antibody to detect the targeted protein. To reduce the number of false-negative results, arrays may be designed in which a number of antibodies directed against different epitopes within the target protein of interest are incorporated. Thus, lower signals would indicate that protein levels are downregulated and that some key epitopes may have been modified. The greatest impediment to antibody arrays is that antibodies vary in their affinity for their specific antigen, thus, requiring extensive preliminary experiments to determine the optimum amount to be spotted.30 Despite these problems, antibody arrays have the makings of a powerful tool for detecting protein markers that change during malignancy and for the early detection and diagnosis of certain cancers. The next two experimental approaches describe a novel methodology for the elucidation of pathways involved in cancer initiation, progression, responsiveness to treatment, acquired drug resistance, and novel drug discovery. A complete identification and understanding of the deregulated pathways that lead to tumourigenesis is severely lacking and remains crucial to our understanding as many signaling pathways responsible for cell proliferation, growth, and differentiation are affected in cancer progression. Nucleic Acid Programmable Protein Arrays (NAPPA). The standard method for generating protein arrays is to produce proteins in an in vitro assay, purify them, and then to spot them individually onto the solid support. In target arrays, these proteins are then tested for their interaction with other molecules including proteins, drugs, antibodies, or for their role as substrates. The method is labor-intensive, technically difficult, and expensive. As an alternative to the standard design of protein arrays, Ramachandran et al. developed a target-based array known as a nucleic acid programmable microarrays (NAPPA), essentially a form of self-assembling microarray (Figure 3A).33 This methodology relies on the ability to express a collection of well-characterized cDNAs simultaneously in vitro thus allowing one to circumvent many of the problems associated with the purification/preparation of proteins. The advantage of the NAPPA approach is the ability to generate the protein right on the slide. The method is based on the anchoring of biotinylated plasmid DNA, which encodes a GST-tagged target fusion protein, to an avidin-coated glass slide.33 A polyclonal anti-GST antibody is simultaneously immobilized onto each spot in close proximity to the plasmid DNA. Subsequently, the GST-fusion mRNAs and proteins encoded by the biotinylated plasmids are expressed in an in situ expression system using rabbit reticulocyte lysate and T7 polymerase. The proteins produced are then immobilized on the slide by the polyclonal GST antibody, thus, obviating the need to purify proteins and subsequently attach them to the solid support. The array is subsequently incubated individually with labeled probes of interest thus detecting interactions in a binary fashion. One problem with this approach is that the GST-purification tag, which is bulky, may block important binding domains (epitopes) within the target protein. This may be partially overcome by placing the tag at the other end of the protein, either the N-terminus or the C-terminus, or by adapting a system that uses a smaller epitope tag that may be less likely to sterically interfere with binding. An advantage of the experimental system is that, since it is based on the rabbit reticulocyte system rather than a nonmammalian system, the target proteins are likely to contain some of the normal posttranslational modifications. Journal of Proteome Research • Vol. 5, No. 5, 2006 1055
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Figure 3. Experimental approaches for basic cancer research. (A) Nucleic acid programmable protein arrays (NAPPA). Plasmid DNA is biotinylated and subsequently bound to an avidin-coated solid support as discrete spots. Similarly, an anti GST-polyclonal antibody is bound to the same spots. Proteins encoded by the plasmid DNA are expressed in situ using T7 polymerase to generate template for translation by the rabbit reticulocyte lysate expression system. Expressed proteins are GST-tagged and thus immobilized by the GST-polyclonal antibody. The array is incubated with a single fluorescently tagged probe to find proteinprotein interactions. (B) Living-cell microarray. Plasmid-encoded cDNA or shRNA are embedded in gelatin as discrete spots on a solid support. Transfection reagent and adherent tissue culture cells are added such that the cells which overlay the spots take up the plasmid DNA and either overexpress or down-regulate (RNAi) the encoded protein of interest in defined cell clusters.
In terms of cancer research, the approach may prove fruitful in screening for therapeutic drugs, which would either enhance 1056
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or reduce specific protein-protein interactions that are known to be involved in malignancy. Conceivably, NAPPA may be adapted for the screening of autoantibodies to tumor antigens in the serum of patients. In this method, plasmids containing encoded GST-tagged tumor antigens would be spotted on the array and later probed with either Cy3- or Cy5-labeled serum proteins isolated from patients. Thus, in this case, the method would be modified to screen for the presence of many biomarkers in a nonbinary manner. NAPPA can also be adapted to study the effect of known mutations in signaling molecules that occur during uncontrolled cell proliferation by the simple introduction of lesions (i.e., point mutations) within the coding region of the plasmid DNA sequence thus allowing a comparison of enzyme kinetics, drug binding, and drug specificity between normal and abnormal forms of the protein. Live Cell-Based Arrays. One of the most interesting approaches based on the microarray format involves the use of live cells overexpressing defined human cDNA. Ziauddin and Sabatini spotted mammalian expression vectors encoding specific proteins as discrete spots on gelatin-coated glass slides (Figure 3B).34 Subsequently, adherent mammalian cells (HEK293 cells) were plated together on the slides in the presence of a transfection reagent. This resulted in clusters of cells becoming transfected from the underlying plasmid cDNAs. Thus, hundreds of overexpression systems separated by a lawn of nontransfected control cells could be analyzed at once by morphological and other routine methods. For example, as one endpoint in their study, they used fluorescent microscopy to examine the effect of protein overexpression on the induction of apoptosis with apoptotic cells identified by TUNEL labeling (TdT-mediated dUTP nick end-labeling) which detects the presence of fragmented DNA. In addition, using phosphospecific antibodies, they were able to identify cell clusters with increased levels of phosphotyrosine or elevated levels of activated members of the MAPK pathway. Live cell microarrays may also be adapted to study loss-offunction-induced phenotypic changes in mammalian cell systems via the use of RNA interference.35 Silva and co-workers performed a proof of principle study in which they spotted plasmid DNA encoding a specific short hairpin RNA for GFP and a plasmid encoding GFP together in the same spot on the glass support. Using fluorescent microscopy, they were able to show that the control spots in which the RNAi vector was absent routinely expressed GFP, whereas spots where the RNAi construct was present specifically down-regulated the expression of ectopically expressed GFP. Live cell microarrays have enormous promise. This experimental system is versatile in that it allows the study of proteinprotein and protein-small molecules interactions in the context of whole intact cells as well as to screen and study live cells expressing different transfected proteins and the associated phenotypes.34 Phenotypic analysis, cell proliferation/cell number, and morphology can be carried out using microscopic and immunohistochemical approaches. One can study the effects of overexpression and/or loss of function of targeted proteins and their involvement in cell cycle progression, apoptosis, and phosphorylation/dephosphorylation. Overall, this experimental approach will allow the identification of genetic suppressors, enhancers, and synthetic lethal interactions. Live cell microarrays have several advantages when compared to protein arrays. They do not require the in vitro expression and subsequent purification of individual proteins. As a result, the limitations of protein stability, post-
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Figure 4. Tissue array. Initially, patient tumour samples are histologically analyzed for heterogeneity by hematoxylin and eosin staining. Subsequently, selected regions are removed as core biopsies and assembled in an oriented fashion into a recipient block. This tissue microarray block is sectioned, and the sections are then deposited onto a glass support. The multiple arrays of the core biopsies which are generated allow probing at the proteome level with specific antibodies, at the genomic level with FISH, and the transcriptome level with RNA probes by in situ hybridization.
translational modifications, and folding are not an issue. An additional benefit is that the plasmid DNA printed on the slides is stable for months. In the future, the live cell microarrays promise a powerful method for high-throughput analysis of functional effects of overexpression and/or depletion phenotypes of numerous proteins in a defined cellular environment. This methodology may provide a tool to map signaling pathways and complex cellular processes such as resistance to cancer drugs. Tissue Microarrays. With respect to cancer research, a major limitation of gene expression profiling has been the ability to survey the expression profile of only one tumor at a time (either at the genomic, transcriptional, or translational level). To profile numerous cancer specimens in a high-throughput fashion, the histological analysis of malignant tissues in an array format was developed (Figure 4).36 The basic method involves taking individual core samples of individual paraffin-embedded biopsies from numerous patients and arraying them in a new recipient block from which sections can be made.36 To reduce the problem of intra-tumor heterogeneity, the method requires that multiple core biopsies be taken from representative areas of the paraffin-embedded tissue (after hematoxylin and eosin staining) for analysis.37 This innovative array-based approach allows for the simultaneous analysis of a variety of tumor types, patient samples, and different stages of tumor progression as well as the histological analysis and/or multilevel analysis of numerous tissue samples simultaneously. Depending on the method of tissue preservation (such as fixation in cold ethanol prior paraffin embedding), tissue samples can be analyzed at various levels, that is, at the DNA level by fluorescence in situ hybridization (FISH), at the mRNA level by the use of specific riboprobes for in situ hybridization, and at the protein level by immunohistochemistry with antibodies against the protein of interest.36 This experimental approach has been validated in several clinopathologic analysis of clinical patient samples. Hoos et al. used tissue microarrays to immunohistochemically profile 59 fibroblastic tumors for
expression levels of Ki67, p53, and the retinoblastoma protein (pRB).38 Ginestier and co-workers examined 55 clinically and pathologically homogeneous breast tumors for 15 proteins and compared protein levels as determined in their tissue array results to mRNA levels as determined by a cDNA array analysis.37 Interestingly, there was no correlation between cDNA and tissue arrays for two-thirds of the proteins studied with the protein levels displaying prognostic value. Recently, Abd El-Rehim et al. analyzed almost 2000 patient breast carcinoma biopsies using a tissue microarray approach.39 Immunohistochemistry of core tissue biopsies was performed with a large panel of biomarkers previously identified as important discriminator genes for distinct groups of breast cancer using cDNA microarray analysis. Contrary to the original four groups identified in the cDNA microarray clustering analysis, six groups were identified based on a large panel of distinct protein expression patterns. Since breast cancer is heterogeneous at the molecular level with several clinically distinct behaviors, this type of study may prove useful for the development of group-specific therapeutic treatments. Tissue arrays provide a platform to analyze numerous patient samples for alterations in the levels of specific biomarkers. They have become a routine method used for prognosis of prostate cancers40,41 and have confirmed prognostic markers for bladder cancer42 and renal cell carcinomas.43 They are thus an invaluable aid to both the oncologist and pathologist. These results further emphasize the fact that tissue microarrays, while not useful in identifying new biomarkers, are a beneficial tool in validating candidate markers discovered in DNA-based microarray experiments. The two methods together will ultimately lead to better identification, characterization, and prognosis of disease with an enhanced understanding of the critical factors involved in cell regulation.
Conclusion and Future Prospects Array technology, with the help of sophisticated analytical tools, not only allows the collection of a vast amount of gene Journal of Proteome Research • Vol. 5, No. 5, 2006 1057
reviews expression data, but also offers many advantages for delving into the intricate changes that occur in malignancy. With the implementation of novel approaches, researchers will simultaneously be able to gather large pools of biological information and to use this vast collection of data to portray each cancer type as a more complete picture. The rewards from developing these methods are great. They may enable better preventative care by identifying individuals predisposed to cancer due to the presence of particular set(s) of marker genes or methylation patterns within key genes. With respect to clinical applications, microarray platforms will promote an increased incidence of obtaining the appropriate diagnosis which bodes well for better prognosis, as the corresponding treatment can be initiated. In addition, microarrays allow for the evaluation of those therapies that best treat each cancer type, thereby improving the chances of the patient’s survival.44-48 Combining the use of DNA microarrays and tissue microarrays has raised the bar in the recent cancer studies. Before the advent of tissue microarrays, cDNA arrays were useful in identifying molecular markers or characteristic gene expression patterns to a particular type of cancer. However, the use of DNA-, protein-, tissue-, and live cell-based arrays promises to take us several steps further, potentially allowing the subsequent testing of these molecular signatures across a gradient of tissue samples from a variety of cancer types in a population. Therefore, this will potentiate the classification of similar cancer types and distinguish these types from those that were based on the gene expression data from the DNA arrays. However, at the moment, the applications of these methodologies to standard screening techniques in a medical laboratory is not only cost prohibitive, but still under assay development. Refinement, simplification, and automatization of these approaches will provide a cheaper alternative and eventually allow for routine clinical screens. Such data will also allow researchers to diligently correlate the gene expression patterns with the cellular pathways that lead to tumorigenesis. Knowing the genetic basis of particular tumor types will further facilitate cancer therapy. Continued improvement of the analytical methods and software for all flavors of DNA, protein, and tissue microarrays will further enhance the information gained in these cancer studies. One can envision that drug design will be tailored for specific molecular targets that take into account the underlying heterogeneity in tumors on an individual patient level. Unlike the traditional methods for tumor classification which are based on morphology, presence/absence of metastasis, and the degree of differentiation, novel microarray analysis for treating cancer at both the genomic and proteomic levels will lead to better patient treatment.
Acknowledgment. The Microarray infrastructure is
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supported by research grants from the Canadian Foundation for Innovation (CFI) and Ontario Innovation Trust (OIT). Dr. J. W. Hudson is supported by operating grants from NCIC (Terry Fox Foundation) and NSERC and research equipment grants from CFI, OIT, NSERC, and NCIC. A. Kozarova is a recipient of an NSERC graduate scholarship.
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