Copy number variants and pharmacogenomics

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1 For reprint orders, please contact: REVIEW Copy number variants and pharmacogenomics Karim Ouahchi 1,2, Neal Lindeman 1,2 & Charles Lee 1,2 Author for correspondence 1 Brigham and Women s Hospital, Department of Pathology, 20 Shattuck Street, Thorn 612A, Boston, MA 02115, USA Tel.: ; Fax: ; clee@ rics.bwh.harvard.edu 2 Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA Keywords: copy number variants, drug response, duplication/deletion, genetic variation, metabolizing enzyme, pharmacogenomics The earliest pharmacogenomic studies focused on highly penetrant sequence polymorphisms in drug-metabolizing enzymes. The recent discovery of the widespread occurrence of copy number variants/polymorphisms in the human genome holds promise for new pharmacogenomic discoveries, aside from the commonly used single nucleotide polymorphism approach. Here we review the discovery of copy number variants and speculate on their implications for pathophysiology and pharmacogenomics. Pharmacogenomics in medicine & clinical pharmacology The relationship between genetic determinants and drug response was established nearly 50 years ago, with the discovery that deficiency in glucose-6-phosphate dehydrogenase (G6PD) results in hemolytic anemia following ingestion of primaquine [1]. In 1960, Evans and colleagues demonstrated inherited differences in isoniazid acetylation, resulting in slow inactivator and rapid inactivator phenotypes [2]. More recent investigations have shown single nucleotide polymorphisms (SNPs) in genes encoding important metabolizing enzymes (such as the cytochrome P450 enzyme superfamily) to be associated with clinical phenotypes of drug efficacy/toxicity. These and other ongoing studies are resulting in an increasing list of important associations touching nearly all medical specialties [3 5]. However, the utilization of pharmacogenomics in day-to-day practice has lagged behind its potential, and most current therapeutic monitoring still relies upon the quantitation of drug and/or metabolites in the patient s blood. Fully integrating pharmacogenomic testing in clinical practice will require health practitioners to know the relationship between genotypes and clinical outcomes, and to have specific data that can be used to modify drug selection or dosage. The Human Genome Project has provided a critical tool for generating this information: a reference sequence upon which DNA sequence variation can be cataloged [6] and subsequently associated with different phenotypes. Despite the availability of the sequence data, our collective understanding of the form and scope of interindividual genetic variation is still in its infancy. While phenotypic associations with sequence variants in metabolizing enzymes have been established, it is believed that SNPs alone may not predict all phenotypes, and therefore certain drugs may require studies involving other aspects of genomic variation, to explain interindividual variations in efficacy, metabolism, and/or toxicity. Copy number variants (CNVs), a recently described class of genomic variation that is distinct from SNPs, hold promise for such new pharmacogenetic discoveries. Copy number variants: discovery, definitions & database Recent studies have demonstrated that apparently healthy, unrelated individuals have several hundred segments of their genomes that exhibit variation in the number of copies present [7 9]. Iafrate and colleagues applied a 1 Mb resolution, comparative genomic hybridization (CGH) array platform to study 39 healthy, unrelated individuals, in whom they found 255 loci containing CNVs, with an average of 12.4 CNVs per individual [7]. Sebat and colleagues used a modified array-cgh technique, representational oligonucleotide microarray analysis (ROMA), and reported similar findings: 76 CNVs with an average of 11 CNVs per individual [8]. Both studies suggested that at least half of these CNVs contained coding sequence. Shortly thereafter, Tuzun and colleagues used a computational method to compare the reference human genome sequence to end sequence data available from clones in a fosmid DNA library, and identified 241 CNVs: 139 gains and 102 deletions [9]. Their method of analysis identified DNA variants ranging from 8 40 kb in size, with approximately 40% of the variants mapping to gene introns or exons. The vast majority (over 80% of the CNVs identified in this study) had not been previously reported, owing partially to a size selection bias for smaller CNVs by their computational strategy / Future Medicine Ltd ISSN Pharmacogenomics (2006) 7(1),

2 REVIEW Ouahchi, Lindeman & Lee These and other CNV discovery studies probably only represent the tip of the iceberg as they either sample only a fraction of the genome and/or a limited number of individuals. The real number of CNVs per genome is probably much higher; with some estimates now being as high as 140 in a given individual [Carter N, Jones K, Scherer S, Lee C. Unpublished Data]. A public CNV database [101] now serves as a repository for cataloging CNVs from published studies. More than 600 CNV loci are currently entered into this database. For each CNV, the listing includes known genes that have been mapped within each region. The proteins encoded by these genes have diverse functions, including metabolism, signal transduction, neurotransmission, cell adhesion, and immunological response. Recent completion of Phase I of the HapMap project [6], which cataloged 1.3 million SNPs among the 269 individuals chosen to identify common genetic variation in people, provided data for McCarroll and colleagues to uncover over 500 new putative deletion variants [10]. Remarkably, whole genes were also found to be deleted, and in some cases both copies of a gene were deleted among these apparently normal individuals. Some of the high frequency, homozygously deleted genes (e.g., glutathionine S-transferase µ1 [GSTM1], GST θ1 [GSTT1], cytochrome P450 [CYP]2A6, and uridine diphosphate-glucuronosyltransferase [UGT]2B17) are involved in cellular/drug metabolism, reinforcing the notion that certain copy number variants may influence a person s ability to metabolize/detoxify specific reagents to which they are exposed. Copy number variants & pathogenesis CNVs, like other structural chromosomal rearrangements, may arise from nonallelic homologous recombination (NAHR) and unequal crossing-over events. If the recombination event occurs between genes, they would be expected to result in deletion and duplication events, without disruption of the genes themselves (Figure 1A). For example, Charcot-Marie- Tooth disease type 1A (CMT1A) and hereditary neuropathy with pressure palsies (HNPP) are caused by a recombination event occurring within a 24 kb low copy repeat that shares 98.7% nucleotide sequence identity [11]. However, if the recombination event occurs within a gene, the structure, and most likely the function, of that gene is disrupted (Figure 1B). For example, a 9.5 kb DNA sequence in intron 22 of the coagulation Factor VIII is repeated twice at a region near the long arm terminus of the X chromosome. An intra-chromosomal recombination event involving this repeated region causes disruption of the Factor VIII gene and half of hemophila A cases [12]. If the repeated elements are located on different (nonhomologous) chromosomes, such recombination events could lead to chromosomal insertions or translocations. Each of these structural alterations, in turn, can lead to altered protein expression or function with subsequent phenotypic effect. Both deletions and duplications can lead to dosage imbalance of genetic material, and therefore affect the expression levels and activity of the protein [13]. Inversions and translocations could lead to protein truncation, altered gene expression (by juxtaposing coding sequence from one protein to the promoter region of another protein), or creation of a novel fusion protein (containing functional domains from two different proteins). Examples of copy number variant genes CNVs of glutathione transferase genes GSTM1 and GSST1 are detoxification enzymes belonging to the glutathione S-transferase (GST) supergene family. Deletion variant GSTM1 and GSST1 alleles have a frequency within the Caucasian population of approximately 50 and 20%, respectively [14,15]. Since GST enzymes are involved in the detoxification of reactive metabolites of carcinogens, it has been suggested that functional GST variants associated with less effective detoxification of carcinogens may lead to increased susceptibility to cancer [15]. Inherited variants of GSST1 and GSTM1 have been correlated with variable risk and different clinical outcomes in various malignancies, including acute leukemia [16] and carcinomas of the lung [17] and stomach [18]. By extension, chemical substances that are metabolized by GST family enzymes (such as halomethane) may exhibit differences in toxicity or efficacy in patients with different copy numbers of these genes. CNVs of the CYP2D6 gene CYP2D6 was the first example of genetic polymorphism identified in the superfamily of CYP enzymes [19]. Individuals were described as poor, non-, or extensive metabolizers based on the measurement of urinary metabolites of debrisoquine after being administered the parent compound, which is used in the treatment of hypertension. Each phenotype observed was correlated to a specific genotype. More than 46 major polymorphic CYP2D6 alleles have now 26 Pharmacogenomics (2006) 7(1)

3 Copy number variants and pharmacogenomics REVIEW Figure 1. One possible mechanism for the generation of CNVs. A. B. Duplication or deletion Cross-over event Disruption Some CNVs may arise from nonallelic homologous recombination events. (A) If the recombination event occurs between genes, a deletion product and a duplication product would be expected. (B) If the recombination event occurs within a gene, the structure (and most likely the function) of that gene would be disrupted. CNV: Copy number variant. been reported, and their frequencies vary among different ethnic groups [20]. CYP2D6 enzymes process more than 30 prescribed and over-thecounter drugs, including antiarrhythmics, antihypertensives, β-blockers, monoamine oxidase inhibitors, antipsychotics, and antidepressants. Although much of the focus has been on SNPs in CYP2D6, CNVs may play a role as well, as suggested by a study reported by Ledesma and colleagues [21] who genotyped 737 unrelated Caucasian individuals and found CYP2D6 CNVs in approximately 12% (5.1% with deletion [CYP2D6*5] and 7.2% with duplication [CYP2D6 2 ] variants). Focused investigations into the role of CNV in drug metabolism have not yet been fully realized. However, these few examples of critical metabolism genes associated with the earliest reported CNVs provide a tantalizing suggestion of the potential for important future pharmacogenomic discoveries. Copy number variants of the CCL3L1 gene Duplications in host defense genes, such as chemokines, have been recently investigated in HIV/AIDS susceptibility. Gonzalez and colleagues correlated the number of chemokine (C-C motif) ligand 3-like 1 (CCL3L1) gene copies with susceptibility to HIV in different ethnic populations [22]. CCL3L1, or macrophage inflammatory protein (MIP)-1αp, encodes for an HIV-1 suppresive chemokine and ligand for the HIV coreceptor, CCR5. A low CCL3L1 copy number was found to be associated with enhanced susceptibility to HIV infection and AIDS progression [22]. Other important immunological genes associated with CNVs include certain major histocompatibility complex class II genes (e.g., major histocompatibility complex, class II, DM α [HLA-DMA] and HLA-DM β [HLA-DMB]). The role of these CNVs in infectious and autoimmune diseases has not yet been investigated, but the potential exists for CNVs to impact critical molecules involved in response to injury/infection, which may, in turn, provide a prediction of the potential efficacies of different drugs directed at different molecular targets involved in the disease process. Are CNVs a new genetic marker for pharmacogenomic studies? Studying CNVs is not fundamentally different from studying SNPs. The techniques of detection and genotyping may differ, although the same principles apply. Candidate gene approaches focus on genes that are selected because of an a priori hypothesis regarding their role in the phenotype. Pharmacogenomic studies may focus on specific genes for which a role in drug metabolism has already been established, such as genes belonging to the CYP family, or other important metabolizing enzymes. Since only approximately half of currently listed CNVs overlap with known genes (very few of which are well described in drug metabolism), it will be important to identify more CNVs located in the vicinity of known genes 27

4 REVIEW Ouahchi, Lindeman & Lee Highlights Recent studies have demonstrated the widespread presence of copy number variants (CNVs) in the human genome, with each DNA segment encompassing several to hundreds of thousands of bases of DNA. Ongoing studies suggest that any individual may have over 100 regions in their genome where the copy number of the DNA segments in each region varies from person to person. Half of the CNV reported overlap with genes that are encoding proteins with diverse functions. Functional CNVs genes encoding metabolizing enzymes (such as cytochrome P450 [CYP]2D6, glutathionine S-transferase θ1 [GSTT1] and GST µ1 [GSTM1]), in addition to immunological genes (chemokine [C-C motif] ligand 3-like 1 [CCL3L1]) have been reported. CNVs may be useful in association studies to evaluate candidate genes and to further characterize the molecular basis of drug response. involved in pharmacologic pathways. Drolet and colleagues suggest that a coding SNP in the voltage-gated potassium channel, shaker-related subfamily, member 5 (KCNA5), which is expressed in the cardiac atrium, is associated with antiarrhythmic drug resistance (resistance to block by quinidine) [23]. A polymorphic duplication encompassing the KCNA5 locus has now been reported in the CNV database, suggesting that studies may now be warranted to investigate the potential genotypic effect of CNV associated with KCNA5 and the antiarrhythmic drug response phenotype. The genetic basis of common diseases is thought to be determined by either disease-predisposing alleles with relatively high frequencies (for example, common variants) and variable penetrance or less frequent variants with higher penetrance. Most current association studies in pharmacogenomics rely on SNP assays with subsequent searches for mutations in potential candidate genes. While correlation between highly penetrant mutations and drug response phenotypes has been established for certain metabolizing enzymes, the identification of genes underlying complex traits using association studies and SNPs as genetic markers has been more challenging. Among important parameters to consider in association studies are the marker allele frequency, the extent of linkage disequilibrium (LD) (reviewed in [24]), and the effect of ethnic admixture [25]. Moderate to high frequency allele CNV variants (i.e., more than 10%) are potentially useful in such association studies. Indeed, Iafrate and colleagues demonstrated that among more than 200 CNVs, 24 CNVs were present in more than 10% of the individuals, and these may therefore be more useful for such association studies [7]. As CNVs continue to be discovered throughout the genome, more of these common CNVs are likely to be identified. Furthermore, CNVs could be used in combination with SNPs [10] in order to provide a larger allelic marker frequency spectrum, and to further confirm an association between phenotype and genotype. Conclusions SNPs may not provide a suitable explanation for all pharmacogenomic variation. Recent discoveries have shown the widespread existence of CNVs in the human genome. These CNVs involve some known metabolizing enzymes, such as CYP2D6, GSTM1 and potential drug targets such as CCL3L1, and can influence phenotype through alteration in gene dosage, structure and expression. Moreover, CNVs may enable discoveries of other genetic mechanisms underlying variation in drug response. Clearly, further study in this area is required to demonstrate the full significance of these CNVs in human pharmacogenomics. Acknowledgments The authors would like to thank David Casavant, Steven McCarroll and PJ Perry for critical reading of this manuscript and Shona Hislop for her assistance with Figure 1. Karim Ouahchi is supported by the Brigham and Women s Hospital Department of Pathology training grant. Some of the work presented in this review is currently supported by grants from the Leukaemia and Lymphoma Society (Charles Lee), Genome Canada, and the Wellcome Trust UK. Bibliography 1. Beutler E: The hemolytic effect of primaquine and related compounds: a review. Blood 14(2), (1959). 2. Evans DA P, Manley KA, McKusick VA: Genetic control of isoniazid metabolism in man. BMJ 2, (1960). 3. Mellen PB, Herrington DM: Pharmacogenomics of blood pressure response to antihypertensive treatment. J. Hypertens. 23(7), (2005). 4. Bishop JR, Ellingrod VL: Neuropsychiatric pharmacogenetics: moving toward a comprehensive understanding of predicting risks and response. Pharmacogenomics 5(5), (2004). 5. Martin AM, Nolan D, Gaudieri S, Phillips E, Mallal S: Pharmacogenetics of antiretroviral therapy: genetic variation of response and toxicity. Pharmacogenomics 5(6), (2004). 6. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P: A haplotype map of the human genome, Nature 437(27), (2005). 7. Iafrate AJ, Feuk L, Rivera MN et al.: Detection of large-scale variation in the human genome. Nature Genet. 6(9), (2004). 8. Sebat J, Lakshmi B, Troge J et al.: Largescale copy number polymorphism in the human genome. Science 305(5683), (2004). 9. Tuzun E, Sharp AJ, Bailey JA et al.: Finescale structural variation of the human 28 Pharmacogenomics (2006) 7(1)

5 Copy number variants and pharmacogenomics REVIEW genome. Nature Genet. 37(7), (2005). 10. McCarroll SA, Hadnott TN, Perry GH et al.: Common deletion variants in the human genome. Nature Genet. (In press). 11. Lupski JR: Charcot-Marie-Tooth disease: lessons in genetic mechanisms. Mol. Med. 4(1), 3 11 (1998). 12. Naylor JA, Buck D, Green P, Williamson H, Bentley D, Giannelli F: Investigation of the Factor VIII intron 22 repeated region (int22h) and the associated inversion junctions. Hum. Mol. Genet. 4(7), (1995). 13. Stankiewicz P, Lupski JR.: Genome architecture, rearrangements and genomic disorders. Trends Genet. 18(2), (2002). 14. Pemble S, Schroeder KR, Spencer SR et al.: Human glutathione S-transferase θ (GSTT1): cdna cloning and the characterization of a genetic polymorphism. Biochem. J. 300, (1994). 15. Parl FF: Glutathione S-transferase genotypes and cancer risk. Cancer Lett. 221(2), (2005). 16. Ye Z, Song H: Glutathione S-transferase polymorphisms (GSTM1, GSTP1 and GSTT1) and the risk of acute leukaemia: a systematic review and meta-analysis. Eur. J. Cancer 41(7), (2005). 17. Skuladottir H, Autrup H, Autrup J et al.: Polymorphisms in genes involved in xenobiotic metabolism and lung cancer risk under the age of 60 years. A pooled study of lung cancer patients in Denmark and Norway. Lung Cancer 48(2), (2005). 18. La Torre G, Boccia S, Ricciardi G: Glutathione S-transferase M1 status and gastric cancer risk: a meta-analysis. Cancer Lett. 10, 217(1), (2005). 19. Kimura S, Umeno M, Skoda RC, Meyer UA, Gonzalez FJ: The human debrisoquine 4-hydroxylase (CYP2D) locus: sequence and identification of the polymorphic CYP2D6 gene, a related gene, and a pseudogene. Am J Hum Genet. 45(6), (1989). 20. Ingelman-Sundberg M: The human genome project and novel aspects of cytochrome P450 research. Toxicol. Appl. Pharmacol. 207, (2005). 21. Ledesma MC, Agundez JA: Identification of subtypes of CYP2D gene rearrangements among carriers of CYP2D6 gene deletion and duplication. Clin. Chem. 51 (6), (2005). 22. Gonzalez E, Kulkarni H, Bolivar H et al.: The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science 307(5714), (2005). 23. Drolet B, Simard C, Mizoue L, Roden DM: Human cardiac potassium channel DNA polymorphism modulates access to drugbinding site and causes drug resistance. J. Clin. Invest. 115(8), (2005). 24. Zondervan KT, Cardon LR: the complex interplay among factors that influence allelic association. Nature Rev. Genet. 5(2), (2004). 25. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K: A comprehensive review of genetic association studies. Genet Med. 4(2), (2002). Website 101. The Database of Genomic Variants website

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