preclinical development handbook || genomics

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801 22 GENOMICS Dimitri Semizarov and Eric A. G. Blomme Global Pharmaceutical Research and Development, Abbott Laboratories, Abbott Park, Illinois Preclinical Development Handbook: Toxicology, edited by Shayne Cox Gad Copyright © 2008 John Wiley & Sons, Inc. Contents 22.1 Introduction 22.2 Gene Expression Microarrays 22.2.1 Technology 22.2.2 Applications of Microarrays in Preclinical Development 22.3 Comparative Genomic Hybridization 22.3.1 Technology 22.3.2 Applications of CGH in Preclinical Development 22.4 Gene Silencing 22.4.1 Antisense Oligonucleotides and Ribozymes 22.4.2 Short Interfering RNA 22.5 Conclusion References 22.1 INTRODUCTION In the past five to ten years a growing number of new drugs have been discovered using a target-based approach [1], implying a significant paradigm shift in the phar- maceutical industry. This shift has been facilitated by the decoding of the complete sequence of the human genome [2–4]. Today, a significant portion of the research and development (R&D) effort in the pharmaceutical industry is focused on drug target identification and validation. This paradigm change also requires that new

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Page 1: Preclinical Development Handbook || Genomics

801

22 GENOMICS

Dimitri Semizarov and Eric A. G. Blomme Global Pharmaceutical Research and Development, Abbott Laboratories, Abbott Park, Illinois

Preclinical Development Handbook: Toxicology, edited by Shayne Cox GadCopyright © 2008 John Wiley & Sons, Inc.

Contents

22.1 Introduction 22.2 Gene Expression Microarrays

22.2.1 Technology 22.2.2 Applications of Microarrays in Preclinical Development

22.3 Comparative Genomic Hybridization 22.3.1 Technology 22.3.2 Applications of CGH in Preclinical Development

22.4 Gene Silencing 22.4.1 Antisense Oligonucleotides and Ribozymes 22.4.2 Short Interfering RNA

22.5 Conclusion References

22.1 INTRODUCTION

In the past fi ve to ten years a growing number of new drugs have been discovered using a target - based approach [1] , implying a signifi cant paradigm shift in the phar-maceutical industry. This shift has been facilitated by the decoding of the complete sequence of the human genome [2 – 4] . Today, a signifi cant portion of the research and development (R & D) effort in the pharmaceutical industry is focused on drug target identifi cation and validation. This paradigm change also requires that new

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802 GENOMICS

methodologies be used in preclinical development. The preclinical development process for targeted agents involves genomic analysis of preclinical model systems to elucidate the mechanism of target inhibition, delineate off - target effects, and identify biomarkers.

Genomic technologies may potentially play an important role at all stages of drug discovery. For example, target identifi cation in cancer often involves large scale genomic screens for genes or their products that alter cell proliferation and survival. Identifi cation of genes frequently amplifi ed at the chromosomal level may reveal a target for therapeutic intervention, whose inhibition by small molecules or antibodies may deprive the cancer cell of its proliferation/survival advantage. Because gene amplifi cation often leads to overexpression [5 – 7] , the search for genes overexpressed in cancer cells is a logical and common strategy for target identifi cation.

Genomic technologies are now frequently utilized in lead selection and com-pound optimization. They are routinely used to profi le candidate compounds in order to identify the pathways activated and thus delineate the on - target and off - target effects. Large databases are being created to correlate the chemical structures of compounds and their toxicogenomic profi les. An important component of pre-clinical development is biomarker discovery. The increasing importance of biomark-ers is closely connected with the exponential growth in R & D costs currently experienced by the pharmaceutical industry [8] . An early discovery of an effi cacy biomarker could substantially decrease development costs and cut the development time, thus allowing an earlier market entry and hence an improved patent life cycle [9] . Therefore, biomarker discovery programs are now incorporated early into the discovery process, typically at the lead optimization or preclinical testing stages. So - called patient stratifi cation biomarkers, that is, markers correlated with the disease type or response to a drug candidate, are particularly valuable in oncology development, as cancer represents a heterogeneous genetic disease. In one of the recent successful examples of patient stratifi cation strategies, the response to a drug called Herceptin was found to correlate with the amplifi cation of the HER2 gene [10 – 12] . These biomarkers offer the possibility of rationally selecting patients for clinical trials and are now being pursued early in the discovery process. For example, cell culture screens for target inhibition are often accompanied by detailed genomic analysis of the cell lines to identify genetic markers correlating with the response in the assay.

Several genomic technologies are used in biomarker discovery. Gene expression and CGH microarrays are used to determine the molecular profi les that predict the outcome in various cancers and correlate with the response to the drug (for exam-ples see Refs. 13 – 21 ). Microarrays can also be utilized to determine the molecular signatures of the response to the agent, leading to discovery of effi cacy biomarkers [22] .

In the following sections, we describe the current state of the aforementioned microarray technologies, as well as several methods for manipulating gene activity, and review the applications of these tools in preclinical drug development. The objective of this chapter is to introduce to the reader the most common genomic technologies, to describe the relevant protocols, and to briefl y summarize their applications in preclinical drug development. Chapter 24 comprehensively reviews the applications of DNA microarrays in the fi eld of toxicology.

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22.2 GENE EXPRESSION MICROARRAYS

22.2.1 Technology

We defi ne some of the microarray terms used in this chapter.

Probe. A DNA fragment attached to the microchip and used to detect transcripts in the test sample.

Reverse Transcriptase. An enzyme capable of synthesizing DNA using RNA as a template.

DNA Polymerase. An enzyme capable of synthesizing DNA on a DNA template.

cDNA. DNA synthesized off an mRNA template. cDNA. RNA synthesized in an in vitro transcription reaction using cDNA as a

template.

Standard Microarray Protocol Generally, gene expression microarrays represent microchips containing thousands of DNA probes, which are used to analyze the abundance of multiple transcripts in a sample. Based on the type of probe, micro-arrays can be classifi ed into oligonucleotide and cDNA arrays. While cDNA micro-arrays may potentially offer higher sensitivity of mRNA detection, diffi culties in manufacturing and deposition of cloned and purifi ed long DNA sequences have limited the use of cDNA arrays largely to academic laboratories. In this chapter, we focus on the more commonly used oligonucleotide microarrays.

Figure 22.1 presents an outline of a typical microarray experiment. Total RNA is isolated from the test sample using one of the known techniques [23, 24] and is used as a template to synthesize cDNA. An oligo(dT) primer with an attached T7 sequence is used as a primer for reverse transcription. An enzyme called reverse transcriptase catalyzes the synthesis of cDNA using the input RNA as a template. The resulting cDNA is then subjected to one round of DNA replication to generate double - stranded DNA. This reaction is catalyzed by a DNA polymerase. The resulting double - stranded DNA then serves as a template for T7 RNA polymerase, which recognizes the T7 sequences in the cDNA [25] . The in vitro transcription is per-formed in the presence of biotinylated rNTPs to label the cRNA. The cRNA is fragmented and hybridized to the array.

After array hybridization, the array is washed to remove unbound molecules, stained using streptavidin - phycoerythrin and a bioinylated anti - streptavidin anti-body, and scanned in a fl uorescent scanner in order to quantify the signal for all the probes. After the acquisition of the image by the scanner, a specialized program overlays a grid on the array to identify the spots and generates a table of signal intensities. A different program then processes the signal intensities for individual probes to generate the intensities for each individual gene, determine the back-ground, and perform normalization. The signal intensity for each gene serves as a measure of the abundance of the corresponding transcript in the initial sample.

The quantity of the test RNA sample is an important factor in microarray analy-sis. The RNA polymerase synthesizes multiple copies of cRNA from each cDNA molecule, and the target preparation protocol results in an amplifi cation of the original sample. For RNA quantities > 1 μ g, the above - described protocol typically

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804 GENOMICS

produces enough cRNA for at least one or two array hybridizations. Another impor-tant consideration is the integrity of the sample. If the total RNA is degraded, the reverse transcriptase will not be able to synthesize suffi ciently long cDNAs and the cRNA products will not hybridize to all the probes for the transcript. Although 3 ′ - bias is an important consideration in microarray probe design, many probes on a microarray are removed by hundreds of nucleotides from the 3 ′ - terminus of the transcript. Probes that are distant from the 3 ′ - terminus will not hybridize if the cRNA products are of insuffi cient length. Similarly, suboptimal functioning of the reverse transcriptase may lead to shortened cDNAs and cRNAs and hence will result in an underestimation of the abundance of the transcripts. As microarray applications expand, more different sample types will need to be analyzed. Next, we consider two special situations with regard to the sample type.

Microarray Analysis of Archived and Small Samples The standard expression microarray protocol requires high quality intact RNA. However, as microarrays became an important tool in biomarker research, the researchers started looking for ways to apply the microarray technology to retrospectively analyze archived human tissue samples. Freezing of a sample immediately after surgical resection typically preserves RNA. Therefore, such samples can be analyzed with a standard microarray protocol. It is critical that the sample be frozen immediately, because even quick manipulation of tissue results in changes in gene expression [26, 27] that

FIGURE 22.1 Gene expression profi ling using DNA microarrays (single - round RNA amplifi cation protocol). The total RNA sample is amplifi ed to generate biotin - labeled cRNA, which is fragmented and hybridized to a microarray.

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may be mistaken for true characteristics of the sample. However, the most diffi cult challenge is presented by formalin - fi xed paraffi n - embedded (FFPE) samples, as formalin fi xation results in irreversible modifi cation and degradation of RNA [28, 29] . It is noteworthy that formalin fi xation results in a wide spectrum of RNA modi-fi cations, including cross - linking, addition of monomethylol residues to the nucleic bases, and adenine dimerization [28] . Until recently, such samples were deemed unsuitable for microarray analysis. In 2003, a specialized microarray for analysis of FFPE samples was developed [30] . It contains mostly probe sets that are directed against the three hundred 3 ′ - terminal nucleotides of the transcripts (instead of the 600 nucleotide limit set for regular microarrays). The increased 3 ′ - bias is intended to facilitate binding of shortened cRNAs synthesized off truncated cDNAs. However, a solution remains to be found for analysis of highly modifi ed RNA, as RNA with modifi ed nucleic bases has a limited capacity to produce cDNA in the reverse tran-scription reaction. The chip is designed for use with a reagent system [31] , which enables RNA isolation from FFPE tissues as well as its amplifi cation and labeling.

Signifi cant progress has also been made in analysis of small samples. The progress in this area was fueled by the introduction of a tissue dissection technology called laser capture microdissection [26, 32] . The technique involves placing a transparent fi lm over a tissue section and selectively adhering the cells of interest to the fi lm with a fi xed - position, short - duration, focused pulse from an infrared laser (Fig. 22.2 ). During the procedure, the tissue is visualized microscopically. The fi lm with the

FIGURE 22.2 [32] Laser capture microdissection protocol. (a) A tissue section is mounted on a microscope slide and covered with transparent fi lm. Cells of interest are selected visually under a microscope. (b) A laser beam focused on the cells of interest is activated, causing the fi lm to adhere to the selected cells. (c) The fi lm is removed together with the attached cells. At this point, the cells of interest can be lysed and further processed.

(a)

(b)

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Infrared beam focallyactivates adhesive layer laser

Transfer of selected cells

Selected field of cells

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806 GENOMICS

procured tissue is then removed from the section and used to isolate DNA or RNA [32] . As laser capture microdissection of tissue samples became common, an urgent need arose for a microarray protocol suitable for analysis of samples ranging from 100 to 1000 cells. The problem was solved by introducing an additional round of RNA amplifi cation. In a two - round amplifi cation protocol (Fig. 22.3 ), the fi rst round is performed with regular rNTPs, while the second round uses labeled rNTPs as in the single - round amplifi cation protocol described in the previous subsection. Today, protocols involving laser capture microdissection and RNA isolation from single cells followed by gene expression analysis have become routine. They made possible the analysis of pure tumor cells and comparison with adjacent normal tissue [33 – 35] .

Microarray Data Analysis Most microarray experiments involve either a com-parison between a treatment and the baseline or a comparison between the test sample and a reference. Therefore, the fi rst level of data analysis almost inevitably involves building gene expression ratios, that is, calculating the ratios between the intensity values for the same gene from two different chips. A t - test is typically used to determine the signifi cance of the difference between the control and the test values for each gene. The data can then be fi ltered to remove insignifi cant changes. Methods based on conventional t - tests provide the probability ( p ) that a difference in gene expression occurred by chance. It is common to set up a signifi cance thresh-old at p - value ≤ 0.01. Although p = 0.01 is a reasonably stringent cut - off for experi-ments designed to evaluate small numbers of genes, a microarray experiment measuring the expression of 20,000 genes (such as an experiment using Affymetrix U133A arrays) would identify 200 genes by chance.

To reduce the number of false positives, signifi cance analysis of microarrays [36] can be used. This method identifi es genes with statistically signifi cant changes in expression by assimilating a set of gene - specifi c t - tests. Each gene is assigned a score on the basis of its change in gene expression relative to the standard deviation of repeated measurements for that gene. Genes with scores greater than a threshold are deemed potentially signifi cant. The percentage of such genes identifi ed by chance is the false discovery rate (FDR). To estimate the FDR, nonsense genes are identi-fi ed by analyzing permutations of the measurements. The threshold can be adjusted to identify smaller or larger sets of genes, and FDRs are calculated for each set. Other false discovery analysis methods have recently been introduced, some of which include analysis of false negatives [37, 38] .

To improve the robustness of analysis, multiple replicates of the same sample are typically run. The commonly accepted minimum is two replicates; however, use of triplicates minimizes the false - positive rate [39] . A microarray experiment consists of multiple steps, and each step represents a potential source of variation. The varia-tion of the measured gene expression data can be categorized into two generic sources: biological and technical variations. The biological variation in gene expres-sion measured comes from different animals or different cell lines or tissues. It refl ects the variability in gene expression between the different biological samples used in the experiment. Biological variation can be assessed only by using indepen-dent biological replicates. If all biological samples are pooled, the biological varia-tion is minimized, but the potentially useful information on the variability in gene expression between different animals or cells is lost. The technical variation accounts

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808 GENOMICS

for the variation associated with the use of microarray techniques unrelated to the biological samples. The biological, technical, and residual variations are mutually independent. The variation in a measured intensity is the sum of these two variations.

The contributions of the technical and biological variabilities to the overall vari-ability have been studied extensively. It has been established that the biological variation is the main component of the variation between microarray experiments [40, 41] . Therefore, biological replicates (multiple plates of cells, multiple animals, etc.) rather than technical replicates (multiple arrays run for the same sample) should be used whenever possible.

Once the data are fi ltered with respect to the statistical signifi cance, an additional fi lter is usually set up to remove genes with a small fold change, which are less likely to be biologically relevant. As the robustness of microarrays improved with time, the fold change threshold was lowered; it is now commonly set at 1.5 or 2.

Simple lists of genes regulated as a result of a biological process provide limited information. As applications of expression microarrays widened and the numbers of genes analyzed increased, the analysis methods have become more and more complex.

One of the most common tasks in microarray data analysis is identifi cation of common patterns of gene regulation in a population of samples. An example of such a task would be identifi cation of genes coinduced in a series of treatments or dis-covery of genes associated with a particular biological characteristic of the samples (disease category, tissue type, etc.). Problems of this type are commonly solved by two - dimensional clustering, a statistical procedure whereby samples (each repre-sented by one or more microarrays) are aggregated into clusters based on the simi-larity of their expression “ signatures, ” while the genes are simultaneously clustered based on the similarity of their expression levels across the samples. The rationale behind clustering samples according to their expression profi les is simple: samples with similar gene expression “ signatures ” are more likely to have common biological characteristics. Similarly, genes coregulated in a series of samples are more likely to be part of a common biological pathway activated in the samples under consider-ation. Thus, two - dimensional clustering may provide very useful information on the degree of relatedness between samples and reveal the genes potentially relevant to the classifi cation. Clustering results can be conveniently visualized using a gene expression matrix, or a heatmap, in which each column represents an experiment and each row represents a gene (Fig. 22.4 ). Each element of the heatmap is colored based on the expression level, thus providing a convenient visual representation of the gene expression patterns across all the experiments. One of the most notable applications of clustering is in cancer classifi cation, which was pioneered in the late 1990s (for examples see Refs. 18 and 42 – 44 ).

If clustering is done without any a priori introduced sample classifi cation, it is referred to as unsupervised clustering. Because of its unbiased nature, unsupervised clustering is often used to identify patterns in previously unclassifi ed complex data-sets. Several unsupervised clustering algorithms are used for microarray data analy-sis (reviewed in Refs. 45 and 46 ). Hierarchical clustering is the most common algorithm. It uses an agglomerative approach, whereby expression profi les are suc-cessively joined to form groups based on similarity between them, thus forming a hierarchical tree, or dendrogram [47] . The latter presents a convenient visualization

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GENE EXPRESSION MICROARRAYS 809

option and is often presented together with a heatmap (Fig. 22.4 ). An alternative algorithm is k - means clustering, a divisive approach based on partitioning the dataset into a predefi ned number ( k ) of clusters [48] . Obviously, it requires some a priori knowledge of the biology of the dataset so that the number of clusters could be preset. When the researcher can specify in advance not only the number of clus-ters but also the relationships between them, self - organizing maps (SOMs) can be used, which organize the clusters into a “ map ” where similar clusters are close to each other [49] .

Unsupervised algorithms can fi nd novel patterns in datasets but are not designed to classify data according to known classes. On the contrary, supervised clustering approaches, such as support vector machines (SVMs) [50] , take known classes and create rules for assigning genes or experiments into these classes. The user initially runs microarrays for a training set with known class labels and enters the gene expression profi les together with the classifi cation information into the algorithm. This “ trains ” the algorithm or teaches it to associate certain gene expression patterns with the predefi ned sample class labels. The next step is to profi le samples from a new set of samples, the test set, and input the gene expression data into the algo-rithm. The latter will then classify the samples using the knowledge on class – expres-sion pattern associations learned from the training set. SVMs have been used to identify genes with similar expression patterns, but their most powerful application is in classifi cation of samples. They have been used extensively in cancer classifi ca-

FIGURE 22.4 An example of a heatmap obtained by hierarchical two - dimensional cluster-ing of nine samples. Each row represents a sample and each column represents a gene. (Although not visible here, a red color is used for upregulation and a blue color is used for downregulation of genes, with black reserved for unaffected genes.) The dendrogram on the left illustrates the degree of relatedness between the expression profi les of the samples and the dendrogram on the top refl ects the similarity of the expression levels for each gene across the samples.

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tion and in some cases proved to be more reliable than the traditional diagnostic methods [42, 51] . Examples of supervised clustering relevant to drug discovery are discussed in Section 22.2.2 .

The gene expression pattern of a cell refl ects its phenotype and may provide infor-mation on the intracellular signaling pathways functioning in the cell. A comparison of the gene expression profi les for diseased tissue and the adjacent normal tissue may provide insight into the mechanisms and pathways driving the disease. In in vitro experiments, studying the gene induction patterns caused by a treatment may help identify the pathways activated or repressed by the treatment. A prerequisite for all these applications is the ability of the researcher to map gene expression profi les to signaling pathways, that is, identify the associations between the affected genes and the known pathways for the entire signature. There are several programs that allow association of gene expression patterns with predefi ned biological classes [52 – 60] . They use one of the existing gene classifi cation systems, such as Gene Ontology [61, 62] , Biocarta [63] , or KEGG [64] , to determine the enrichment of an expression sig-nature in a certain motif, such as “ cell cycle control ” or “ DNA biosynthesis. ” Gene Ontology is the most commonly used annotation system, which classifi es a signifi cant fraction of the genome ( ∼ 15,000 genes) according to their involvement in a biological process or molecular function or their cellular localization. It is built hierarchically and involves a parent – child relationship between its terms. Programs such as Map-pFinder allow the researcher to identify the GO terms that show correlated gene expression changes in a microarray experiment. The affected GO terms can then be rank - ordered based on the Z - score, a statistic that refl ects the number of genes in the term meeting the criteria for fold change in the microarray experiment [52] . Map-pFinder was one of the fi rst GO - based programs designed for analysis of gene expression data. It has since been used to study the effects of various factors on intracellular pathways in vitro [65, 66] and in viv o [67] . Combined use siRNA - mediated gene silencing and MappFinder analysis of expression signatures has been suggested as an approach to pathway profi ling [65] . Other pathway analysis pro-grams have been developed in the past several years, which allow convenient visual-ization of the pathway analysis results [55 – 60] . Many of them use manually curated pathways instead of Gene Ontology, which permits greater focus and lower redun-dancy, especially when studying specifi c disease - related pathways.

Given the large amounts of gene expression data accumulated in the literature, integrative analysis of multiple datasets related to the same disease represents a very attractive idea. The precedent for such analysis was established when Rhodes et al. [68] performed so - called meta - analysis of four different gene expression data-sets for prostate cancer. The authors identifi ed a molecular signature common to the datasets, thus generating a robust signature of the disease. The signature was then mapped to KEGG pathways [64] to reveal a common biological motif, activa-tion of polyamine biosynthesis [68] . Other studies identifi ed common gene signa-tures in different breast and lung cancer datasets [69 – 71] . The existence of common motifs in datasets from different laboratories despite the well - publicized problem of interplatform variability presents strong evidence in favor of microarrays as tools for identifi cation of drug targets and biomarkers.

As the amount of information derived from microarrays continues to increase, new and more complex data analysis procedures will emerge that will facilitate the current and future applications of the technology.

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22.2.2 Applications of Microarrays in Preclinical Development

The applications of gene expression microarrays in drug discovery are summarized in Fig. 22.5 . Chronologically, the fi rst application of gene expression microarrays in drug discovery was in the area of identifi cation of therapeutic targets.

Target Identifi cation and Validation One of the most obvious and logical strate-gies for target identifi cation is a genome - wide scan for genes or their protein prod-ucts overexpressed in the diseased tissue relative to its normal counterpart. Gene expression microarrays are ideally suited for this purpose because they provide wide genome coverage and permit effi cient screening of hundreds of samples using a standardized reproducible protocol. To date, they have been most widely used to identify drug targets in cancer.

The development of sophisticated data analysis methods, such as two - dimensional clustering, has permitted cancer classifi cation on the genomic basis and identifi cation of groups of genes overexpressed in subsets of target cancers. Hema-tological cancers were historically the fi rst group of cancers to be subjected to microarray classifi cation, because of the better availability of homogeneous tumor cell populations. In a pioneering study of 72 acute leukemia samples, Golub et al. [42] identifi ed a gene expression signature that reliably determined the disease type. The signature correctly assigned the samples into the acute myeloid leukemia (AML) or acute lymphoid leukemia (ALL) categories and further determined the subtype in the ALL category (B cell and T cell). In another study, Yeoh et al. [72] profi led 360 ALL blasts and identifi ed gene expression patterns associated with the 6 known clinical subgroups of disease. While these and other works produced robust gene expression - based disease classifi ers, no members of these classifi ers appeared suitable as therapeutic targets. A recent exemplary microarray study of the mixed - lineage leukemia (MLL) [73, 74] went one step further. Armstrong et al. [73] not only identifi ed a distinct gene expression profi le that distinguishes leukemias with MLL translocations, but also identifi ed a single gene that is consistently overex-pressed in this leukemia type and validated it as a therapeutic target [74] . Mixed - lineage leukemia has traditionally been defi ned as a subtype harboring specifi c chromosomal translocations, so - called MLL translocations. The fi rst study estab-lished a globally distinct nature of this disease by identifying its characteristic gene expression profi le [73] . The authors then analyzed the MLL signature and identifi ed

FIGURE 22.5 Applications of expression microarrays at different stages of drug discovery and development.

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one gene consistently overexpressed in most MLLs compared to other acute leu-kemias [74] . This gene codes for a receptor tyrosine kinase, FLT3, which plays an important role in hematopoietic development. The authors then determined that many MLLs also carry mutations in the FLT3 gene, which result in constitutive activation of the kinase. An inhibitor of FLT3 was shown to differentially kill cells carrying MLL translocations in vitro and inhibit MLL development in a mouse model, thus validating FLT3 as a therapeutic target for MLL. This study truly dem-onstrates the power of gene expression microarrays in drug target discovery.

Microarrays have also been used for target identifi cation is solid tumors. Expres-sion profi ling of medulloblastoma, a highly invasive tumor of the cerebellum, revealed expression signatures of the metastatic and nonmetastatic subtypes of the disease [75] . Several genes, including PDGFRA and members of the downstream RAS/mitogen - activated protein kinase pathway, were found to be upregulated in the metastatic tumors. The study thus yielded several potential therapeutic targets. Dhanasekaran et al. [76] have profi led over 50 normal and neoplastic prostate samples and three common prostate cell lines. Characteristic expression signatures were identifi ed for localized prostate cancer, metastatic hormone - refractory pros-tate cancer, and benign prostatic hyperplasia. Two genes were consistently overex-pressed in prostate cancer samples, hepsin (a transmembrane serine protease) and PIM1 (a serine/threonine kinase). The expression of the protein products of these genes was examined on a tissue microarray containing 738 tissues and found to be elevated in prostate cancer. An independent microarray screen of 11 malignant and 4 normal prostate samples also revealed hepsin overexpression in prostate cancer and implicated hepsin as a promising drug target [77] . Other studies later confi rmed the conclusions on hepsin as a promising therapeutic target [78] .

It is important to note here that target identifi cation by microarrays should not be limited to direct identifi cation of genes overexpressed in diseased tissue. Most genes in the disease signature encode proteins that are not druggable. Moreover, overexpression of certain genes in a disease signature only represents the bottom part of the signaling pathway modulating the disease process. Recent bioinformatics developments described in the preceding subsection permitted mapping of gene expression signatures to signaling pathways. Thus, an alternative approach to target identifi cation may involve identifi cation of pathways activated in diseased tissue and a search for druggable targets within the pathways. Subsequent target validation steps may involve modulation of the target activity in vitro followed by microarray profi ling to determine whether the target - related signature is affected.

All the aforementioned studies have been performed in patient samples. In many cases patient samples are not available and therefore target discovery has to be performed in preclinical model systems, such as cultured cells or animal models of disease. However, these systems may produce a lot of noise, because cultured cells accumulate a lot of secondary genetic changes (such as gene amplifi cations/dele-tions), which are not relevant to the initial disease - originating events. Therefore, microarray - based target discovery in cultured cells requires inventive experimental design and careful validation. In an example from the oncology area, Sarang et al. [79] performed a rescue screen in neuroblastoma cells for approximately 900 known therapeutic agents. They found that 26 of these agents are capable of rescuing the cells from oxidant stress. The compounds were profi led by microarrays to identify the common gene expression signature. One of the genes in the signature codes for

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a secreted peptide called galanin. In a series of validation experiments, galanin was found to reverse cell death cased by oxidant stress. It was thus concluded that galanin receptor may represent a therapeutic target. Indirect identifi cation of thera-peutic targets has been performed by analyzing pathways activated by expression of known oncogenes [80, 81] .

Although microarrays can reliably associate gene overexpression with disease, they cannot tell whether the overexpression is the cause or an effect of the disease. In most cases, microarray screens generate long lists of overexpressed genes and thus necessitate complex hypothesis - driven follow - up experiments aimed at select-ing the genes that have a causative role in the disease process. Target validation is often performed in model systems using a loss - of - function or gain - of - function approach. Microarrays can also be used at this stage to assess the global effects of target knock - down or target overexpression. An example of such application is a study by Cho et al. [82] , who knocked down the protein kinase RI α gene with an antisense oligonucleotide in cultured cells and subjected the cells to microarray analysis. It was found that suppression of the protein kinase RI α gene causes coor-dinated changes in gene expression that can be mapped to cell growth, differentia-tion, and activation pathways. The genes that compose the proliferation – transformation signature were downregulated, whereas those that defi ned the differentiation sig-nature were upregulated in antisense - treated cancer cells and tumors, but not in host livers, thus validating the mechanism of protein kinase RI α . Short interfering RNA (siRNA) has recently emerged as a promising tool in target validation [83, 84] . Gene silencing with siRNA followed by microarray experiments and systematic pathway analysis may prove to be a powerful strategy in functional genomics. Early studies established the feasibility of this approach [65, 85] . One should expect that this approach will be actively used in target validation in the near future.

Compound Characterization More recently, DNA microarrays have become an important tool in compound selection and optimization. Since microarrays are capable of generating a genome - wide view of the physiological state of the cell, they are well suited for characterization of compounds and elucidating their mechanism. A global approach to the problem would be to create a database of expression sig-natures associated with compound treatments in a relevant model system (Fig. 22.6 ). If such database is suffi ciently populated with compounds with known mechanisms, one could envision its use as a look - up table: a series of novel compounds can be profi led by microarrays and their expression signatures can be plugged into the table. One of the available clustering algorithms would then be used to determine which of the known compounds have the most similar signatures to the test compounds.

Although more effort is needed to compile a comprehensive activity/expression signature database, numerous studies have used microarray profi ling to determine the mechanism of known and novel agents. In an early example of microarray - based compound profi ling, Glaser et al. [86] profi led three known histone deacetylase (HDAC) inhibitors in two cell lines to generate an HDAC inhibition signature. The gene expression signatures of the three active HDAC inhibitors were generally similar to each other and differed signifi cantly from the signatures for inactive ana-logues, suggesting that the expression signatures are mechanism based. A core sig-nature of 13 genes was identifi ed that was common to all the HDAC inhibitors and

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GENE EXPRESSION MICROARRAYS 815

all the cell lines used. The inhibitors were structurally different, implying that the signatures refl ect the HDAC inhibition mechanism rather than off - target effects of the compounds.

An inhibitor of cyclooxygenase 1 (COX1) and cyclooxygenase 2 (COX2), sulin-dac sulfi de, has been profi led in colorectal carcinoma cells, which express the COX1 enzyme but little COX2 [87] . A group of 11 genes was identifi ed; their expression was further analyzed in another colon cancer cell line, HCT116, which expresses very low levels of both enzymes. The drug did not affect the expression of these 11 genes in HCT116, suggesting that their induction is COX dependent. This study is important because COX inhibitors have been shown to induce growth arrest and apoptosis of colon cancer cells. Elucidation of the mechanism of this potential anti-cancer effect could result in either selection of one of the known COX inhibitors for preclinical studies or optimization of the structure and creation of new COX inhibitors with potential anticancer activity. More compound profi ling studies are analyzed in a very comprehensive review by Clarke et al. [88] .

In an example from a different therapeutic area, Gunther et al. [22] have profi led multiple classes of antidepressants, antipsychotics, and opioid receptor agonists in primary human neurons. The gene expression patterns obtained for these groups of drugs were then used to construct statistical models capable of predicting drug effi -cacy. Several supervised classifi cation algorithms were shown to reliably predict the functional group of each of the drugs based on their expression signatures.

A similar approach can be taken with respect to toxicity if the appropriate model system is used (hepatocytes, liver of model animals, etc.). This application of DNA microarrays will be comprehensively covered in Chapter 24 . The toxic mechanism of a new compound can be determined from the database by association with the toxicity signatures of known compounds. In an early proof - of - concept study, Waring et al. [89] treated rats with 15 different known hepatotoxins, which are known to cause a variety of hepatocellular injuries including necrosis, DNA damage, cirrhosis, hypertrophy, and hepatic carcinoma. Gene expression signatures of the livers of treated rats were clustered and compared to the histopathology fi ndings and clinical chemistry values. The results show strong correlation between the histopathology, clinical chemistry, and gene expression profi les induced by the agents. Other studies also demonstrated the feasibility of predicting the toxicity of compounds using microarray gene expression signatures [90 – 94] . A recent publication describes the creation of a comprehensive chemogenomics database [95] . The authors have pro-fi led approximately 600 known compounds, including over 400 FDA - approved drugs in up to seven different tissues of rats. It was demonstrated that gene expres-sion signatures, when used in conjunction with the database, can be used to predict the toxicity of a compound.

Biomarker Discovery Biomarker identifi cation is a major application for DNA microarrays in preclinical development, which often parallels and aids compound optimization. A biomarker is “ a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention ” [96] . An early identifi cation of a useful biomarker could result in substantial savings in the development process. Therefore, a substantial amount of effort has been devoted to biomarker discovery at the preclinical development stage, using model systems, such as cultured cells and

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816 GENOMICS

animal models. Studies similar to the ones described in the previous subsection may yield useful pharmacodynamic biomarkers related to the mechanism. However, if the goal is to identify patient stratifi cation biomarkers — that is, biomarkers that can predict a patient ’ s response to the drug — one needs to screen basal levels of expres-sion and then relate them to the sensitivity or resistance of the system to the drug. Early attempts to correlate gene expression signatures with drug sensitivity have been done in cultured cell lines [97 – 99] . Gene expression microarrays have been used to generate basal expression profi les of the 60 cell lines used by the NCI for cancer drug discovery screens (NCI - 60 panel) [97] . The gene expression profi les were then correlated with the sensitivity of the cells to several common drugs, such as 5 - fl uorouracil and l - asparaginase [98] . The authors then correlated the expression of certain genes to the mechanisms of drug resistance. Gene expression signatures were used to predict the chemosensitivity of the cell lines. Staunton et al. [99] devel-oped an algorithm to predict the sensitivity of the NCI - 60 cell lines to 232 known agents. All 232 compounds were profi led in the 60 cell lines and the data were divided into a training set and a test set. The training set was used to develop clas-sifi ers and the test set was used to test the accuracy of the classifi ers. The study yielded accurate classifi ers ( p ≤ 0.05) and thus proved the feasibility of chemosen-sitivity prediction by microarrays.

Correlations between drug sensitivity and expression signatures have also been determined in more complex model systems, such as animal cancer models [100, 101] . Zembutsu et al. [100] profi led 85 cancer xenografts derived from nine human organs. The xenografts, implanted into nude mice, were examined for sensitivity to nine anticancer drugs (5 - fl uorouracil, 3 - [(4 - amino - 2 - methyl - 5 - pyrimidinyl)methyl] - 1 - (2 - chloroethyl) - 1 - nitrosourea hydrochloride, adriamycin, cyclophosphamide, cisplatin, mitomycin C, methotrexate, vincristine, and vinblastine). The authors identifi ed gene expression signatures, which correlated with drug sensitivity of the xenografts, and established an algorithm to calculate the drug sensitivity score based on the gene expression pattern.

Studies of this type have established the feasibility of predicting response to the drug based on the expression profi le. However, it remains to be proved that correla-tions established in model systems will reproduce in patient samples. To date, only a limited number of studies correlated drug response with the basal expression profi le in human samples [20, 21, 102 – 104] . Cell culture and xenograft - based models have a number of limitations. Most importantly, cultured cells undergo multiple rounds of selection, acquire additional genetic abnormalities, and therefore may poorly represent the original tumor, both in terms of their genomic patterns and their drug sensitivity profi le. Future research will show whether gene expression profi les obtained in preclinical model systems can be used to predict patient response in clinical trials.

22.3 COMPARATIVE GENOMIC HYBRIDIZATION

22.3.1 Technology

Chromosomal aberrations are detrimental events associated with a number of developmental diseases, such as Down syndrome. Amplifi cations and deletions of chromosomal regions occurring in somatic cells are believed to be one of the main

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factors leading to cancer. Although fl uorescent in situ hybridization (FISH; for a recent review, see Ref. 105 ) has been effectively applied to analyze known genetic aberrations for decades, until recently there was no method for detecting gene copy number alterations on a whole - genome scale. Comparative genomic hybridization (CGH), a technique that enables genome - wide analysis of chromosomal aberra-tions, was fi rst described by Kallioniemi and colleagues in 1992 [106] . The method involves hybridization of the test DNA (sometimes mixed with reference DNA) to a complete representation of the genome attached to a solid support. Originally, CGH was performed on metaphase chromosome spreads, but in the past fi ve to seven years, microarray - based CGH has become dominant (reviewed in Refs. 107 and 108 ). An overview of the array CGH procedure is shown in Fig. 22.7 .

The original array CGH protocols have employed a two - color hybridization scheme, whereby the test DNA is labeled with a red fl uorescent dye, while the refer-ence normal DNA is labeled green. Although genomic DNA can be labeled and hybridized directly, many CGH protocols involve a PCR - based amplifi cation step. Once the DNA is labeled, the test and the reference samples are mixed and hybrid-ized to the array. Cot - 1 DNA is typically added to suppress the hybridization of

FIGURE 22.7 Two - color procedure for comparative genomic hybridization. The test gDNA is labeled red and the reference normal gDNA is labeled green. The gDNA samples are mixed and hybridized to a CGH array. Cot - 1 DNA is added to eliminate the signal from repetitive sequences. After hybridization, the array is washed and scanned to generate signal intensities for all regions of interest.

1.0

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COMPARATIVE GENOMIC HYBRIDIZATION 817

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818 GENOMICS

repetitive sequences. After hybridization, the array is washed and scanned to gener-ate the fl uorescent intensity values for each probe on the array. The data are then normalized and presented as ratios of test/normal (usually on a log scale). An example of the CGH output for one chromosome in one sample is shown in Fig. 22.7 . Ratios between the test and reference samples for multiple positions on a chromosome provide information on the copy number for each region measured. The copy number profi le of a sample typically consists of a series of plateaus cor-responding to regions with a constant copy number, fl anked by abrupt transitions. An important limitation on the use of CGH is that it can measure changes in copy number, but it cannot detect balanced chromosomal translocations or changes in ploidy.

The value of CGH arrays increases with an improvement in genome coverage, resolution, and reproducibility. Several types of array platforms are currently used for CGH. Historically, the genome was represented on CGH arrays as a collection of bacterial artifi cial chromosomes (BACs) (e.g., see Refs. 109 and 110 ). Thousands of BACs were propagated and used as templates to generate PCR products. The PCR products were purifi ed and deposited on a microarray. BAC arrays provide suffi ciently high sensitivity to detect single copy amplifi cations and deletions [109] . The main drawback of the earlier BAC arrays was low resolution. Spotting 2460 BAC clones in triplicate provided an average resolution of 1.4 MB across the genome [109] . However, a high density BAC array was recently developed that contains approximately 30,000 clones arranged in a tiling fashion and covering the entire genome [109] . The array provided a signifi cantly higher resolution and made possi-ble detecting amplifi cations as small as 300 kb [109] .

Arrays containing cDNAs have been used extensively for CGH [4, 6, 111, 112] . The advantages of cDNA arrays include higher reproducibility, easier manufactur-ing, and better representation of the genome [108] . However, multiple probes are required to detect small copy number changes, and more sample needs to be used (several micrograms), due to the lower sensitivity of the array [108] .

A signifi cant breakthrough was achieved in 2004 when two oligonucleotide - based platforms were developed for CGH [113, 114] . One of them was a microarray con-taining over 21,000 60 - mer probes synthesized in situ by an ink jet technology [113] . The array provided a signifi cant improvement in resolution and was shown to reli-ably detect single - copy losses, homozygous deletions, and various types of amplifi ca-tions. It used the two - color protocol outlined in Fig. 22.7 , with the addition of a PCR step to amplify the test and control DNAs. The other platform represented a high density microarray originally designed for detection of single nucleotide polymor-phisms (SNPs) [114] . The array covered over 10,000 SNPs distributed across the genome. Each SNP was interrogated by multiple 25 mers synthesized in situ by a photolithographic method. Unlike the two - color CGH protocol described in Fig. 22.7 , the SNP array protocol involves labeling DNA by incorporation of biotinylated dNTPs (Fig. 22.8 ). After array hybridization, the array is stained using streptavidin - phycoerythrin and a biotinylated anti - streptavidin antibody. Signal intensities from individual SNP measurements are smoothed across a user - defi ned smoothing window using a specialized algorithm. The resulting values are then compared to a preloaded reference dataset for normal DNA to produce an estimate of the copy number in the experimental sample. The array was used to evaluate chromosomal aberrations on a genome - wide scale in a number of cancer cells. It reliably detected

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chromosomal amplifi cations as well as homozygous and hemizygous deletions simultaneously with LOH detection [114] . The arrays produced results generally comparable with those obtained on BAC and cDNA arrays, but the authors reported a substantially lower noise level and a much higher resolution, averaging approxi-mately 300 kb [114] .

The next generation of SNP microarray has an increased SNP coverage (approxi-mately 114,000 SNPs), which corresponds to a resolution of < 100 kb. The software used for data analysis smoothes the signals for all the SNPs and compares the data with the internal reference dataset for over 100 individuals. The output of the soft-ware is the absolute copy number for each SNP position on the chip [115] . The new microarray has been validated in a large study of 101 lung carcinoma samples (tumors and cell lines) [116] . The increased resolution of the array enabled identifi -cation of several small amplifi cations and homozygous deletions that had not previ-ously been detected by other CGH protocols.

One possible limitation on the use of high density oligonucleotide arrays for CGH is the requirement for high purity of the target tissue in the sample. The SNP array protocol uses a PCR amplifi cation step, which may produce nonlinear ampli-

FIGURE 22.8 Gene copy number analysis using SNP genotyping microarrays. The genomic DNA sample is digested with a restriction endonuclease for complexity reduction. Adapters are ligated to the restriction fragments and the fragments are amplifi ed by PCR. The PCR products are denatured, labeled, and hybridized to a microarray. The array is washed and stained to produce signal intensities for each probe. The data for multiple SNPs are smoothed and compared to an internal control to generate an estimate of the copy number for the chromosomal region.

COMPARATIVE GENOMIC HYBRIDIZATION 819

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820 GENOMICS

fi cation effects and thus diminish the ability of the procedure to detect deletions. In particular, Zhao et al. [114] reported a drop in the accuracy of scoring homozygous deletions with decreasing purity of tumor DNA. For example, the deletion of a small region in a cell line was detectable only with ≥ 90% purity of tumor DNA. Deletion of another region was only detected at 100% purity of tumor DNA. The future will show whether this limitation can be overcome in subsequent generations of oligonucleotide - based CGH arrays, but it should be considered today when making the platform decision.

Most existing CGH protocols require at least 500 ng of input DNA for the label-ing reaction, which is equivalent to approximately 50,000 – 100,000 cells. This often presents a constraint as many clinical specimens are small. Numerous DNA ampli-fi cation methods have been suggested for CGH on small samples [117 – 123] . The type of sample is also an important consideration in array CGH. The easiest type of sample to work with is cell culture, because the isolation of high quality DNA is routine and the cell population is homogeneous. Analysis of frozen tissue samples presents more diffi culties because of the potential sample heterogeneity. For example, tumor samples often contain signifi cant amounts of normal tissue, and this dilutes the signal obtained for aberrations in the tumor. Profi ling of archived FFPE samples by CGH presents the greatest challenges, because of the poor quality of DNA iso-lated from such samples. Fixation protocols used in hospitals typically result in a number of known alterations in DNA, including degradation, cross - linking, and modifi cation or loss of nucleic bases [124 – 126] . The average fragment size of DNA decreases with increasing fi xation time [127] . The concentration of formalin used for tissue fi xation and the age of the sample also affect the quality of the genomic DNA preparation [128] .

Archived tissue samples represent an invaluable resource for genetic analysis because the existence of large banks of FFPE tissues with clinical annotation makes possible retrospective analysis of correlation between the genomic profi le of the disease and the outcome or response to treatment. This goal undoubtedly justifi es the amount of effort devoted to the optimization of FFPE CGH protocols. Addition-ally, the task of genomic analysis would be signifi cantly facilitated if the fi xation protocols used by hospitals were standardized, thus eliminating the variation in the DNA quality. Obviously, a protocol minimizing DNA degradation would be preferred.

22.3.2 Applications of CGH in Preclinical Development

Unlike gene expression microarrays, CGH has not yet become a mainstream tool in drug discovery, mostly because it was not commercially available until recently. However, we believe that CGH has a great potential to become a dominant tool in the next decade, particularly in cancer. Indeed, CGH detects alterations at the DNA level, which are believed to be fundamental to the disease. Chromosomal alterations are the main genetic feature of cancer; they often play a causative role in tumori-genesis by altering intracellular signaling and gene expression.

In preclinical development of oncology compounds, one frequently has to face decisions on selecting the appropriate in vitro and in vivo model systems. The genetic heterogeneity of cancer necessitates stratifi cation of patients on the basis of the predominant genetic aberrations. The susceptibility to novel targeted agents corre-

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lates with the presence and the amount of the target, which may in turn be deter-mined by the presence of a genetic aberration. Therefore, the genomic profi le of the preclinical model system needs to refl ect that of the target patient population. For example, Herceptin, an approved agent for the treatment of breast cancer, was developed using cell lines with a HER2 amplifi cation as a model system [129, 130] .

Herceptin acts by binding to the HER2 receptor and causing its internalization and the blockage of signal transduction [10 – 12, 131] . The HER2 gene is frequently amplifi ed in breast cancer; its amplifi cation status is determined by FISH [132] . When a monoclonal antibody was developed against the extracellular domain of HER2, it was fi rst tested on cultured breast cancer cells [129] . Of six mammary carcinoma cell lines tested, only the lines with HER2 amplifi cation (SK - BR3, MBA - MB - 175, and MDA - MD - 361) were sensitive to the antibody. The established corre-lation between HER2 amplifi cation and sensitivity to Herceptin was later used in the clinical development of the drug [10, 11] .

In most cases, however, in vitro and in vivo model systems for compound screen-ing are used without consideration for their genomic profi le. Most commonly, oncol-ogy drug candidates are screened using cultured cancer cells and rodent xenografts that comprise the same cell lines grown subcutaneously in immunocompromised mice. One issue with using tumor cell lines is that the cells have been cultured on plastic for many generations, have acquired additional chromosomal aberrations, and therefore are not representative of the original tumor. In most cases, the main (and the only) selection criterion for cells to be used as model systems is their tissue origin. Such indiscriminate selection of model systems is one of the factors behind the current low success rate in the clinic [133, 134] .

Most transformed cell lines used in preclinical development possess multiple secondary chromosomal aberrations that have been acquired during their propaga-tion on plastic. These aberrations are not refl ective of the genomic profi le of the tumor of origin, but they may affect the sensitivity of cells to drugs. We have recently screened 23 small cell lung carcinoma cell lines previously used for compound screening in vitro and detected numerous large chromosomal amplifi cations and deletions that have not been detected in the target tumor [135] . In other studies, concurrent CGH screening of primary tumors and cultured cell lines of the same origin revealed gene copy number changes in the cell lines that were not present in the tumor samples [116] .

In the near future, the adoption of whole - genome CGH screens should enable rational selection of model systems based on their genomic profi les. The fi rst step in the selection process should be a comprehensive analysis of the genetic hetero-geneity of the target population (Fig. 22.9 ). Model systems would then be selected whose genomic profi les refl ect those found in the patient population (e.g., primary cell lines established from tumors). If the selected model systems vary in their sen-sitivity to the lead compounds, their genomic profi les need to be correlated with the sensitivity/resistance status. One of the established statistical procedures (e.g., analy-sis of variance, or ANOVA) can be used for this correlation. Once genomic features are identifi ed that correlate with the sensitivity to the lead compounds, they can be used to stratify patients. If the correlation is confi rmed in the early phases of clinical development, then enrollment of patients into Phase III should be based on the presence of this key genomic feature. Patient stratifi cation based on genomic bio-

COMPARATIVE GENOMIC HYBRIDIZATION 821

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822 GENOMICS

markers would improve the response rate in clinical trials and decrease the duration of the trial and its cost.

A number of recent studies have proved the feasibility of comprehensive profi l-ing of cancers for gene copy number. For several types of cancer, correlation has been established between the genetic profi le and the outcome or susceptibility to existing treatments. For example, neuroblastoma, a childhood tumor derived from neural crest cells, displays remarkable genetic heterogeneity, with several well - known recurrent aberrations, such as MYCN amplifi cation, 17q gains, and 1p losses [136, 137] . The presence of these aberrations correlates with the outcome [136, 137] , raising a possibility of patient stratifi cation and creation of targeted therapies. This suggests that any neuroblastoma drug development program would benefi t from a stratifi cation scheme, whereby the model system would be screened for genetic abnormalities and the response to the agent would be correlated with the genomic profi le.

A number of studies have been performed to identify genes amplifi ed in specifi c tumors. Cheng et al. [138] have profi led ovarian and breast cancers using array CGH and identifi ed a recurrent amplifi cation on chromosome 1q22. The amplifi cation was centered on the gene coding for a small GTPase called RAB25. The aberration was associated with decreased survival in both types of cancer. RAB25 was then shown to increase anchorage - independent cell proliferation and suppress apoptosis and anoikis. The authors concluded that RAB25 represents an attractive therapeutic

FIGURE 22.9 Genomics - based selection of model systems for preclinical drug develop-ment in oncology. The genetic heterogeneity of the patient population is examined by com-prehensive genomic profi ling with CGH and expression microarrays. A preclinical model is selected based on the similarity of its genomic profi le to that of the target patient population, in particular, with respect to the key genomic aberrations that affect the sensitivity to the drugs under development. Testing of the compounds in the preclinical models is accompanied by detailed genomic analysis of the models to identify the key genomic features that correlate with the sensitivity to the compounds. The presence of these genomic features will then serve as a criterion for selecting patients for clinical trials.

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target. Chibon et al. [139] used BAC - based CGH arrays to characterize the genomic profi les of malignant fi brous histiocytoma, an aggressive tumor, which shows no distinct line of differentiation. The 6q23 band was found to be frequently amplifi ed in this type of cancer. The authors characterized the genes residing in the amplifi ed region and identifi ed ASK1 (MAP3K5) as a candidate target for therapeutic inter-vention. Tonon et al. [140] profi led 44 lung adenocarcinomas and small cell carcino-mas and determined the most frequent aberrations in these tumors. Two genomic regions, 8p12 and 20q11, were found to be frequently amplifi ed in both types of cancer. Several genes residing in these regions were identifi ed, and their overexpres-sion was confi rmed by quantitative RT - PCR. Ehlers et al. [141] used both CGH and gene expression microarrays to study uveal melanoma and determined that the gain of chromosome 8 correlates most strongly with the expression of DDEF1, a gene located at 8q24. It was shown that overexpression of DDEF1 results in increased cell motility. The authors concluded that DDEF1 represents an attractive therapeu-tic target.

In the past few years, CGH has been used to discover patient stratifi cation bio-markers for several types of cancer. Paris et al. [142] profi led archived tumors from 64 prostate cancer patients, 32 of whom had recurred postoperatively. The authors identifi ed a loss at 8p23.2 that was associated with advanced disease, but most impor-tantly, they discovered a chromosomal gain at 11q13.1 that was predictive of postop-erative recurrence, independent of the stage of the disease. One gene (MEN1) coding for a nuclear protein menin was mapped to the amplifi ed region, and its expression was correlated with disease recurrence, thus establishing the gene as a biomarker of the aggressive recurrent disease. In another study, 35 gastric carcinomas were pro-fi led on ∼ 2400 - element BAC CGH arrays, and the patterns of chromosomal aberra-tions were correlated with the clinical history of the patients [16] . Hierarchical clustering of the CGH profi les revealed three predominant groups. Membership in these groups correlated with lymph node status and survival. Patients from cluster 3 had a signifi cantly better prognosis than patients from clusters 1 and 2. Although no commonly amplifi ed genes were identifi ed, this study clearly demonstrates the power of genome - wide CGH in patient stratifi cation and biomarker discovery.

A study by Wreesmann et al. [143] dealt with papillary thyroid cancer (PTC). Two distinct variants of this disease exist, the more aggressive tall - cell thyroid cancer (TCV) and conventional thyroid cancer (cPTC). A panel of 25 TCV and 45 cPTCs was profi led by CGH and gene expression microarrays to identify genomic features that would distinguish between the two subtypes of the disease. Signifi cant differ-ences were identifi ed in the patterns of chromosomal gains and losses. One gene, MUC1, was of particular interest because it was both amplifi ed and overexpressed in TCV. Its overexpression was confi rmed by immunohistochemistry on indepen-dent TCV samples. Multivariate analysis showed a signifi cant correlation between MUC1 expression level and the outcome of treatment, establishing the gene as a prognostic marker in PTC.

Diffuse large B - cell lymphoma is a highly heterogeneous disease that displays signifi cant diversity with respect to clinical presentation and outcome. Different subtypes of the disease require different therapeutic approaches. A recent report [144] described CGH profi ling of 224 diffuse large B - cell lymphomas previously examined by expression microarrays. The authors reported a high degree of correla-tion between the gene copy number and the expression data and identifi ed a chro-

COMPARATIVE GENOMIC HYBRIDIZATION 823

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824 GENOMICS

mosomal region (3p11 - p12) that provided prognostic information that is statistically independent of the previously built gene expression - based model.

The fi rst steps have been taken toward developing a diagnostic tool based on a CGH array. Schwaenen et al. [145] reported a novel CGH chip that can detect recurrent chromosomal abnormalities in B - cell chronic lymphocytic leukemia (B - CLL). The chip contained a total of 644 DNA elements and covered all the known regions frequently altered in B - CLL, as well as some other B - cell neoplasms. The array was tested and validated in 106 primary B - CLL tumor samples.

Although the bulk of the CGH work has been of an exploratory nature, one can expect that comprehensive CGH profi ling of preclinical development models will become routine in the near future. Once the recurrent aberrations are identifi ed for the tumor type of interest, a focused array similar to that described earlier can be developed to routinely interrogate preclinical model systems and stratify patients in clinical trials according to their genomic profi le. Thus, the use of CGH in preclini-cal development will facilitate development of agents targeted to specifi c patient populations.

22.4 GENE SILENCING

We defi ne some of the gene silencing terms used in this chapter.

Antisense Oligonucletide . A short DNA fragment complementary to the target mRNA sequence.

RnaseH . An enzyme that specifi cally binds to and degrades RNA – DNA duplexes.

Dicer. An RNase III - type endonuclease that processes long double - stranded RNA into siRNA.

RISC . An RNA – protein complex containing an siRNA molecule and effector proteins that unwind the siRNA and catalyze the cleavage of the target mRNA.

Transfection . Transfer of nucleic acids into the cell. Electroporation . Transfection of cells with nucleic acids by using electric current

to increase the permeability of the cells.

High throughput alteration of gene activity has recently emerged as a powerful tool in drug discovery. Gene knock - down screens are often performed in cell culture to identify therapeutic targets, and gene overexpression is a common tool in target validation. In the area of preclinical development, manipulation of the target gene ’ s activity is used in compound optimization and selection. In this section we describe gene silencing, a set of techniques frequently used in drug development to identify therapeutic targets and optimize candidate compounds.

22.4.1 Antisense Oligonucleotides and Ribozymes

Antisense oligonucleotides were historically the fi rst tool for targeted gene silenc-ing. Their initial applications were focused on inhibiting the replication of viruses

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in cell culture [146] . Typically, antisense oligonucleotides ∼ 20 nucleotides in length are used. To increase stability and prevent enzymatic degradation, antisense oligo-nucleotides are usually modifi ed, for example, by introducing phosphorothioate groups [147] . Other common types of modifi cation include morpholino [148] and methoxyethyl [149] groups and peptide nucleic acids (PNAs) [150] .

The mechanism of gene silencing by antisense oligonucleotides is illustrated in Fig. 22.10 . They act by hybridizing to mRNA and causing RNaseH - mediated deg-radation of the transcript or creating a steric hindrance for the enzymes that catalyze translation and splicing [151] .

In more than 20 years since the fi rst use, antisense oligonucleotides have pro-duced notable successes, including an approved therapeutic agent (Vitravene ™ by Isis Pharmaceuticals), but their applications were limited by several drawbacks of the technology. Most importantly, antisense oligonucleotides have revealed signifi -cant nonspecifi c effects [82, 147, 152, 153] . In particular, phosphothioate oligonucle-otides have been shown to bind various proteins and thus cause off - target effects [153] . Some oligonucleotides also induce expression of interferons through binding to Toll - like receptors [154, 155] . The potency of antisense oligonucleotides is deter-mined by many factors, including the type of modifi cation and accessibility of the target. Overall, they are less potent as the more recently discovered gene silencing tools [156 – 159] .

Ribozymes also hybridize to mRNA through Watson – Crick base pairing and catalyze its degradation through hydrolysis of the phosphodiester bonds (reviewed in Ref. 160 ). The most commonly used type is the hammerhead ribozyme. The cata-lytic sequence of a ribozyme is fl anked by sequences complementary to the target transcript, a feature that provides target specifi city. The accessibility of the target sequence is an important factor, which determines the potency of silencing [161] .

22.4.2 Short Interfering RNA

Mechanism of Gene Silencing A breakthrough in the fi eld of functional genomics was achieved with the discovery of RNA interference in the late 1990s. In 1998, Fire et al. [162] showed that double - stranded RNA causes specifi c gene silencing when injected into Caenorhabditis elegans . Several years later it was found that in Dro-sophila melanogaster embryo extracts, long double - stranded RNA is cleaved into

FIGURE 22.10 Mechanism of gene silencing by antisense oligonucleotides. Binding of an antisense oligonucleotide to the target mRNA results in target degradation by RNaseH and translational repression due to steric hindrances.

GENE SILENCING 825

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826 GENOMICS

fragments containing approximately 22 base pairs by an enzyme complex called Dicer [163] . This observation prompted the fi rst use of synthetic RNA fragments to induce RNA interference. Elbashir et al. [83, 84] demonstrated that chemically synthesized 21 and 22 nucleotide RNA duplexes cause degradation of homologous mRNA when introduced into cultured cells. These duplexes were named short interfering RNAs (siRNAs). The mechanism of gene silencing by siRNA is illus-trated in Fig. 22.11 (for a recent review, see Ref. 164 ). Long dsRNA is processed in the cell by the Dicer. The enzyme cleaves long dsRNA strands into fragments 21 – 28 nucleotides long, which contain two - nucleotide overhangs. The siRNA duplexes are then incorporated into an enzymatic complex called RNA - induced silencing complex (RISC). Within the silencing complex, the siRNA duplex is unwound in an ATP - dependent manner. Once the duplex is unwound, the antisense strand guides the

FIGURE 22.11 Mechanism of gene silencing by siRNA. Long double - stranded RNA is cleaved by the Dicer complex. The resulting siRNA binds to the RISC complex and is guided to the target site to degrade the target mRNA.

AAAAA

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P

P

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RISC complex to the target site on the mRNA. The latter then causes endonucleo-lytic cleavage of the target mRNA sequence [164, 165] .

In a typical in vitro siRNA experiment, the researcher transfects cells with chemi-cally synthesized siRNA duplexes (typically 22 nucleotides long with two - nucleotide overhangs on both ends) and 24 – 48 hours later measures the effects of the siRNA on the target levels by either QPCR or Western blot. Alternatively, a vector coding for a so - called short hairpin RNA (shRNA) can be transiently or stably transfected into cells to produce a more lasting silencing effect [166] . Several reports have addressed the issue of differential siRNA potency [167, 168] . Duplexes that target different regions of the same gene vary signifi cantly in their potency. The potency of siRNA is likely to be determined by the primary and secondary structures of the target site [164] . More research is necessary to clearly defi ne the rules for designing potent siRNAs.

Another important consideration in gene silencing is the specifi city of the silenc-ing tool. In the case of siRNA, the low concentrations used and the enzymatic nature of its mechanism are factors that diminish the potential for nonspecifi c effects. There are several types of nonspecifi c effects that could be displayed by siRNAs, including (1) degradation of mRNAs other than the target due to cross - hybridization, (2) binding to cellular proteins in a sequence - specifi c manner (aptamer effect), (3) translational silencing through the miRNA effect, and (4) induction of the “ dsRNA response ” that is nonspecifi c with respect to the siRNA sequence. Several recent studies have demonstrated that transfection of siRNA into the cell does not cause global nonspecifi c effects on gene expression [169, 170] . However, other studies have described nonspecifi c effects caused by siRNA, which depended on the concentra-tion, cell type, and the type of siRNA [171, 172] . Overall, the combination of high potency, relatively higher specifi city, and ease of fi nding accessible target sites have quickly made siRNA the preferred gene silencing tool.

Use of si RNA for Target Identifi cation One of the major applications of siRNA is in drug discovery. The initial application was in target identifi cation and validation. A typical siRNA - based target identifi cation screen involves high throughput trans-fection of a large siRNA library into cultured cells and observation of the resulting phenotypes, most often by an established assay. Such screens are most common in cancer drug discovery. Prerequisites for a successful screen include a representative cell culture model, a robust transfection protocol, and a reliable quantitative assay for the desired phenotype. In cancer drug discovery, the desired phenotype is typi-cally cell death, apoptosis, or inhibition of cell proliferation. Other assays are cur-rently being adapted to high throughput siRNA screens, including cell migration, invasion, and colony formation assays. Cells that are hard to transfect, for example, primary cells, may be subjected to electroporation. Electroporation has been shown to be amenable to high throughput screening [173] . While the results of a compre-hensive cancer drug discovery screen are yet to be published, several focused studies have been reported. Berns et al. [174] used a set of retroviral vectors targeting approximately 8000 different human genes for suppression. This RNAi library was used in human cells to identify fi ve new modulators of the p53 - dependent prolifera-tion arrest. Suppression of these genes confers resistance to both p53 - dependent and p19ARF - dependent proliferation arrest and abolishes a DNA - damage - induced G1 cell - cycle arrest.

GENE SILENCING 827

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To facilitate high throughput screening with siRNA libraries, an siRNA microar-ray has recently been developed [175] . The siRNAs are deposited onto a microchip, which is then used for reverse transfection of mammalian cells, thus facilitating high throughput screening of hundreds of genes in multiple cell types. The technique has shown signifi cant promise for drug discovery, but it has several limitations. In par-ticular, only adherent cells can be used in the screen and the transfection conditions need to be optimized for each cell type.

Use of si RNA for Target Validation Short interfering RNA has recently been shown to be an extremely useful tool for target validation. It has been applied to validate targets identifi ed in DNA microarray experiments [176, 177] . Indeed, DNA microarrays identify genes overexpressed in the diseased tissue, but they do not prove the functional involvement of the genes into the disease process. Meanwhile, if silencing these genes with siRNA reverses the disease phenotype, the target can-didate is typically taken to the next stage of validation. In a recent study from the oncology area, Williams et al. [176] used DNA microarrays to profi le colon tumors from 20 patients alongside with normal colon tissue samples. Over 500 genes were found to be consistently overexpressed in one - third of the cancer samples, and 13 of them were confi rmed by quantitative real - time PCR. To identify genes that play an important role in colorectal carcinogenesis, siRNA was used to disrupt expres-sion of several of the overexpressed genes in a colorectal carcinoma cell line, HCT116. Silencing of one of these genes (survivin) severely reduced tumor growth both in vitro and in an in vivo xenograft model, thus validating the target in colorec-tal cancer.

In a recent study, Li and colleagues [178] used siRNA to validate a target in vivo . They used inducible RNA interference in mice to silence hypoxia - inducible factor - 1 α (HIF - 1 α ) in established xenograft tumors. It was shown that HIF - 1 α inhibition results in transient tumor stasis or tumor regression. A differential requirement of HIF - 1 α for tumor growth was also observed among different tumor types. Examina-tion of tumors resistant to HIF - 1 α inhibition suggested that the resistance might result from a less hypoxic tumor environment and that the level of HIF - 1 α expres-sion in tumors may be a useful marker for predicting tumor response to HIF - 1 α inhibition. This study demonstrated the versatility of inducible RNAi as a tool for evaluating cancer targets in vivo .

Overall, siRNA and DNA microarrays are highly complementary technologies, when used successively in the target discovery process. First, DNA microarrays can be used to identify genes overexpressed in a diseased tissue. Then siRNA can be used to silence these genes in a disease model system, while the phenotype is observed. If silencing of a gene affects the disease phenotype, then the gene may indeed have functional involvement in the disease process. This would warrant further investigation, primarily in an in vivo system.

Use of si RNA for Compound Characterization The application of siRNA in compound selection and optimization is rapidly gaining momentum. Several possi-ble schemes have been developed for compound selection that involve combined use of siRNA - based gene silencing and DNA microarrays. siRNA against the chosen target can be used to generate gene expression signatures associated with target knock - down. Candidate compounds can then be screened in the same system and

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their microarray signatures can be compared with that of the siRNA (Fig. 22.6 ). Theoretically, if the compounds inhibit the target, the overlap of the compound and the siRNA signatures should represent the target inhibition signature, while the signatures unique to the compounds represent their off - target effects. In our experi-ence, however, an siRNA against a gene and an inhibitor of the gene product do not always produce similar signatures. This could be explained by the fact that silenc-ing of a gene and inhibiting its product are fundamentally different events. Indeed, inhibitors are often directed against one specifi c site of a protein, leaving the possi-bility that the other sites may still maintain their function or interact with other proteins. Meanwhile, silencing the gene results in the elimination of the entire protein. The time course of the two events is almost certainly different as well: 24 hours after the addition of the inhibitor one should not expect to see the same effects as 24 hours after the siRNA transfection. Additionally, either silencing or inhibition may be incomplete, with the residual activity responsible for the differ-ences in the gene expression effects between the silencing agent and the inhibitor. Nonspecifi c effects of both the inhibitor and the siRNA may also contribute to the divergence. While nonspecifi c effects are expected from the compound, the siRNA is meant to serve as the gold standard of specifi city in this type of experiment. One approach to selecting only the specifi c effects of siRNA is to profi le several siRNAs against the target and then select the overlap of their signatures as the target silenc-ing profi le.

Overall, manipulation of gene activity has become a routine tool in drug discov-ery. Today, it is hard to imagine a target identifi cation or validation strategy without a step that would involve manipulation of the target activity. The approach is becom-ing more common with each new technique developed, as the techniques provide higher potency and specifi city.

22.5 CONCLUSION

In the years following the publication of the human genome sequence, genomic technologies have begun to revolutionize drug discovery and development. Drug development has undergone a major paradigm change, whose essence is in shifting from the trial - and - error approach to an innovative, hypothesis - driven, and system-atic strategy based on target selection and validation, followed by selection and optimization of a compound that would modulate the activity of the target with minimal side effects.

Today, the initial stages of the drug development process, target identifi cation and validation, almost always involve analysis of gene content or expression or manipulation of gene activity. Genomic technologies are also beginning to be used in preclinical and clinical development, following innovative proof - of - concept studies published in the past few years. As we have demonstrated in this chapter, the use of genomics in preclinical development may help bridge the gap between the preclinical and clinical stages of drug development by helping select preclinical models more relevant to the target disease. Thus, genomics may solve one of the most signifi cant problems in today ’ s drug development, the high failure rate in clini-cal trials of drugs that have been selected based on their activity in preclinical systems. One of the implications of this upcoming change for pharmaceutical

CONCLUSION 829

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research and development organizations is the necessity of organizational changes to facilitate close collaboration between discovery and development. Just as the promise of the biomarker research compelled the managers of many pharmaceuti-cal companies to create cross - functional translational biology teams, the new strate-gies for genomics - based preclinical development may necessitate formation of groups dedicated to the genomic analysis of target patient population and the appropriate selection of preclinical model systems. It is not always easy to quantify the benefi ts of genomics in the drug development process. The list of purely genom-ics - based drugs remains short. However, the success of the new drug development paradigm is dependent on the genomic data and therefore there is no alternative to continuing and expanding the use of genomic technologies in drug discovery and development.

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