- Email: [email protected]
Pharmacogenomics
symposium article
Annals of Oncology 18 (Supplement 9): ix24–ix28, 2007 doi:10.1093/annonc/mdm289
Pharmacogenomics S. Marsh Washington University, Division of Molecular Oncology, St Louis, USA
Adverse drug reactions (ADRs) resulting from cancer chemotherapy are estimated to account for 1.9% of overall hospital costs and 15% of overall treatment costs [1]. With multiple drug strategies for many cancer types now approved for use, it is essential to discover markers to identify the most appropriate therapy for each patient. In addition to possible life-threatening ADRs, and the obvious associated trauma and inconvenience for the patient, there is often a narrow window of time for treatment to be effective. Added to this, the cost of some chemotherapy combinations may be prohibitive to some individuals or healthcare providers. Using colorectal cancer as an example, the cost of 5-fluorouracil/leucovorin therapy is approximately 151 times less than the cost of 5-fluorouracil/ leucovorin/irinotecan, and approximately 489 times less than irinotecan/cetuximab [2]. Although the increase in response rate from using combination therapy justifies the expense, the incidence of ADRs and the inter-individual variation in response makes the identification of markers to predict the appropriate therapy combination in advance of treatment selection essential. In addition to environmental and physiological influences, variation in the genetic constitution between individuals will have a major impact on drug activity. Pharmacogenomics encompasses the search for genetic basis for inter-individual differences in drug response [3] with the ultimate goal of personalized therapy selection. Single nucleotide polymorphisms (SNPs) account for the majority of genetic variation in the human genome. The remainder of the variation consists of insertions and deletions (indels), tandem repeats and microsatellites (Table 1). With the completion of the human genome project, there has been a great deal of progress in the discovery, characterization and validation of genetic variation. A variety of genotyping platforms are now available to researchers [4–7], including low throughput e.g. polymerase chain reaction–restriction length polymorphisms (PCR–RFLP) and allele-specific PCR; medium throughput e.g. Taqman, Pyrosequencing, matrixassisted laser desorption ionization time-of-flight (MALDITOF); and high throughput e.g. SNP chips and bead arrays. The availability of resources for fast and accurate polymorphism analysis makes pre-treatment genotyping a realistic possibility.
sources of DNA Pharmacogenomics includes studies of variations in germ-line DNA, somatic mutations and variations in RNA expression [8]. ª 2007 European Society for Medical Oncology
Many studies rely on the availability of DNA and RNA from easily accessible sources, such as blood or saliva [9]. In cancer pharmacogenomics the tumour genome can differ significantly from the germ-line genome. Differences in RNA expression between germ-line and tumour DNA have been documented [10], in addition to regions of chromosome loss, gain and rearrangements, and aberrant methylation [8, 11, 12]. Whilst pharmacogenomic markers in germ-line DNA show high concordance with tumour genotype [13], and markers predictive of toxicity can be usefully assessed in germ-line DNA [9], markers for outcome may not be so well represented in the germ-line genome. Gene amplification and somatic mutations in the tumour genome may be more predictive for outcome to chemotherapy agents than germ-line markers alone [8].
sources of polymorphisms literature There are many published examples of polymorphisms in genes relevant to cancer pharmacogenomics [3, 14–16]. If knowledge of a functional polymorphism in the gene is available (e.g. UGT1A1*28) then this represents an excellent starting point for hypothesis-testing association studies. However, not all genes have been comprehensively screened for polymorphisms and critical information may be missing from the literature for many genes. Often, published pharmacogenomic associations have been performed in small sample sets (<100 patients) and lack validation in large, prospective studies. In addition, significant ethnic variability in genotype frequencies can occur [17, 18], so care should be taken to select polymorphisms from the literature that are known to be present and functional in the representative population(s) of the patient samples to be tested. databases Publicly available polymorphism databases [19] can provide more comprehensive data on variants within genes of interest and therefore they are an essential resource for pharmacogenomic studies, especially where published data is limited. In the May 2006 release (Build 126) of the National Center for Biotechnology Information (NCBI) polymorphism database, dbSNP, there were over 27 million submissions corresponding to over 11 million polymorphisms in the human genome. However, only about half of the polymorphisms in dbSNP have been validated, limiting the number of SNPs that can be confidently included in pharmacogenomic studies without further validation from the investigator [19]. The vast
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
symposium article
introduction
symposium article
Annals of Oncology
Table 1. Glossary of terms
exon flanking region haplotype indel intron linkage disequilibrium methylation
tandem repeat or VNTR untranslated region
amount of data in SNP repositories, coupled with often cumbersome user interfaces can make them difficult to manoeuvre; consequently programs have been developed to allow users to download and manipulate data more easily [20]. More recently, the International HapMap project [21] was initiated to genotype SNPs in multiple world populations (European Caucasian, Chinese, Japanese, Yorubi) to identify regions of linkage disequilibrium and allow haplotype construction across chromosomes and/or gene regions. This data can be utilized in genome-wide association studies to identify novel pharmacogenomic genes. It can also provide a starting point in the form of tagSNPs across a candidate gene where functional polymorphism(s) are currently unknown. To date the HapMap project has deposited over 20 million genotypes from four populations into dbSNP, making this one of the largest publicly available pharmacogenomics resources [21].
resequencing Despite the wealth of knowledge in the literature and public databases, resequencing projects are still identifying many novel variants, especially between different ethnic groups. This suggests that the extent of variation in the human genome is still unknown, and the distribution of those variants among population groups is not fully understood [22]. Consequently, resequencing in diverse ethnic populations is still ongoing in pharmacogenomic studies, and is essential for genes that are currently poorly represented in the databases and literature. In addition, resequencing of the tumour genome can identify somatic mutations that can have a major effect on chemotherapy outcome [23–25]. These mutations are typically not present in the germ-line DNA from patients and consequently would be missed by screening just for previously characterized polymorphisms.
Volume 18 | Supplement 9 | July 2007
location of polymorphisms Functional polymorphisms can be found in any region of a gene; however, the location within the gene will have a correspondingly different functional effect.
exon Polymorphisms within the coding region of a gene, especially non-synonymous polymorphisms, are frequently studied as altering the amino acid sequence is likely to cause a functional difference. Examples of functional non-synonymous polymorphisms include SNPs within the gene encoding thiopurine methyltransferase (TPMT). TPMT methylates 6-mercaptopurine, a drug commonly used in the treatment of childhood acute lymphocytic leukaemia. Reduced TPMT activity is associated with severe haematological toxicity and reduction of mercaptopurine dose is required in patients with low or intermediate TPMT activity [26]. Three common non-synonymous variant alleles (TPMT*2 [amino acid substitution A80P], TPMT*3A [amino acid substitutions A154T and Y240C] and TPMT*3C [amino acid substitution Y240C]) account for up to 95% of low TPMT activity phenotypes [27]. Patients heterozygous for these alleles have intermediate TPMT levels and require approximately 65% of standard mercaptopurine dosage, patients homozygous for the variant TPMT alleles require significant (1/10 to 1/15) mercaptopurine dose reduction [26]. Although synonymous polymorphisms do not alter the amino acid sequence, they can still have a functional effect. They could affect RNA secondary structure [28] or have a role in allelic imbalance and consequently altered gene expression [29]. A synonymous SNP in the DNA excision repair gene, ERCC1, is associated with response to 5-fluorouracil/ oxaliplatin therapy in colorectal cancer patients [30, 31], and response to platinum therapy in ovarian cancer patients [32].
doi:10.1093/annonc/mdm289 | ix25
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
microRNA microsatellite non-synonymous SNP polymorphism resequencing SNP synonymous SNP tagSNP or htSNP
regions of the gene that are translated into amino acid sequence regions upstream and downstream of untranslated regions. Not transcribed into RNA but can contain control element nucleotide sequence along a gene or chromosome region determined by the extent of linkage disequilibrium insertion/deletion: insertion or deletion of one or several bases non-coding regions of a gene. Can be part of alternative spliced forms of the gene non-random inheritance of polymorphisms. A D’ of 1 or –1 represents perfect linkage disequilibrium, a D’ of 0 represents completely random inheritance addition of a methyl group to the cytosine base of CG dinucleotides (CpG islands). Usually occurs in gene promoter regions, leading to transcription silencing short RNA sequences that can regulate gene expression by repression of translation or affecting mRNA stability polymorphic tandem repeats consisting of short stretches of nucleotides (usually 1-4 bases) occurring throughout the genome SNP within an exon that causes an amino acid substitution mutation present in the population at a frequency of at least 1% sequencing the same region of DNA in multiple samples to identify polymorphisms single nucleotide polymorphism: DNA differs between individuals at one base SNP within an exon that does not alter the amino acid sequence marker that provides information for multiple polymorphisms, reducing the amount of genotyping necessary for complete coverage of a gene or chromosome region repeated adjacent sequence, number of bases varies, number of repeats is polymorphic gene region transcribed into RNA but not translated into amino acid sequence
symposium article Although the function of the codon 118 synonymous polymorphism is unclear, this SNP may still be a useful pharmacogenomic marker for predicting response to platinumbased therapy.
5#-untranslated region/flanking region The 5# regions of genes contain regulatory elements (e.g. promoters, enhancer regions) that control gene expression. For example, a polymorphic 28-bp tandem repeat in the 5#untranslated region (5#-UTR) containing a promoter–enhancer region of the thymidylate synthase (TYMS) gene has been identified. This repeat polymorphism (TSER) varies from 2 (TSER*2) to 9 (TSER*9) copies of the repeat, with TSER*2 and TSER*3 being the most common alleles [36]. TSER*3 leads to significantly increased TYMS expression compared with TSER*2 [36]. There have been several clinical studies identifying a role for the TYMS TSER in response to 5-fluorouracil chemotherapy [36]. Patients with rectal cancer were significantly more likely to experience downstaging (a measure of tumour response to treatment) in patients with TSER*2 alleles who were treated with 5-fluorouracil, compared with patients homozygous for TSER*3 [37]. The first genotypeguided clinical cancer trial in North America is currently underway based on TSER genotype. Rectal cancer patients with the TSER*2 allele are treated with standard therapy (5-fluorouracil and radiation). Patients homozygous for TSER*3 are treated with 5-fluorouracil/radiation and an additional chemotherapy agent (irinotecan). Preliminary data suggest an improved response rate for both groups [38]. 3#-untranslated region DNA variations in the 3#-UTR of a gene can have an impact on RNA stability, altering the half-life of RNA and consequently the expression levels of the gene. For example, a 6-bp deletion located in the 3#-UTR, 447 bp downstream from the stop codon, has been identified in TYMS [39]. The deletion has been associated with decreased TYMS mRNA levels in tumour cells, possibly by affecting mRNA stability [40]. The significance of this polymorphism on response to 5-fluorouracil therapy has
ix26 | Marsh
not yet been comprehensively assessed. However, decreasing TYMS mRNA levels in the tumour cell could be hypothesized to lead to an improved response to 5-fluorouracil therapy.
somatic mutations Mutations within the tumour genome could also have pharmacogenomic relevance. Resequencing the exons of the epidermal growth factor receptor (EGFR) gene in tumour DNA from non-small cell lung cancer patients who responded favourably to the EGFR inhibitors gefitinib or erlotinib, identified several somatic mutations in the EGFR gene [23–25]. Conversely, no mutations were identified in patients who did not respond to gefitinib or erlotinib [23, 24]. In addition, paired normal tissue from the same patients did not carry the mutations. Consequently, screening of the patients germ-line DNA would not provide useful information for predicting therapy outcome in this instance, screening of the tumour genome would be necessary [23, 24].
pharmacogenomics in clinical practice The integration of pharmacogenomics into clinical practice is now being realized. In addition to genotype-guided clinical trials [38], the US Food and Drug Administration (FDA) has begun the process of approving the use of pharmacogenomic data for dose selection in cancer treatment. A dinucleotide TA repeat in a TATA box in the UGT1A1 promoter results in altered UGT1A1 expression [41]. The variable number of TA repeats ranges from 5 to 8 copies, 6 TA repeats represents the most common allele (wild-type UGT1A1 expression), and the 7 TA repeat (UGT1A1*28) is the most common variant allele (reduced UGT1A1 expression) [42]. Reduced UGT1A1 expression is linked to an increased risk of toxicity from irinotecan therapy, including severe diarrhoea and neutropenia [43]. A prospective study administered irinotecan to 66 patients with advanced disease. UGT1A1*28 genotype and haplotypes containing UGT1A1*28 were correlated with irinotecan pharmacology and the occurrence of severe toxicity [43]. Patients homozygous for UGT1A1*28 had a significantly greater risk of grade IV neutropenia compared with heterozygous or homozygous wild-type patients [43]. In 2005 the FDA approved a genetic test for UGT1A1*28 [44] and included toxicity and dosing warnings relating to the UGT1A1*28 allele in the irinotecan package insert [45], marking a significant step towards the integration of pharmacogenomics into clinical practice.
polygenic pharmacogenomics strategies Although there are several published examples of clinically relevant polymorphisms, it is clear that assessing a single polymorphism in a single gene will not provide all of the information required to accurately select therapy in advance of treatment [46]. Whilst UGT1A1*28 is clearly associated with the incidence of toxicity from irinotecan, not all patients who experience toxicity carry the UGT1A1*28 polymorphism. Genotyping of additional polymorphisms within UGT1A1,
Volume 18 | Supplement 9 | July 2007
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
intron The vast majority of gene polymorphisms are located within the introns. Although this is often labelled ‘‘junk’’ DNA, intronic polymorphisms can be located within regulatory regions, or within putative microRNA target sites, subsequently affecting gene expression [33]. In addition, they may become coding region polymorphisms within alternative spliced versions of the gene. Polymorphisms located within the intron/exon splice junctions can also cause exons to be skipped by abolishing the splice site. A well-characterized example of a functional intronic polymorphism causing exon skipping is the IVS14+1G>A SNP in the gene encoding dihydropyrimidine dehydrogenase (DPYD). This SNP, also known as DPYD*2A, is located at the first nucleotide of intron 14. The polymorphism causes exon 14 to be skipped and results in a protein with reduced activity [34]. Patients carrying DPYD*2A experience severe life-threatening toxicity from the commonly used chemotherapy drug 5-fluorouracil [34, 35].
Annals of Oncology
Annals of Oncology
acknowledgements Comments and suggestions for this manuscript from Derek Van Booven were greatly appreciated. The author is funded in part by the Pharmacogenomics Research Network (U01 GM63340) and R21 CA113491.
references 1. Wiffen P, Gill M, Edwards J, Moore A. Adverse drug reactions in hospital patients Bandolier Extra. 2002 http://www.jr2.ox.ac.uk/bandolier/Extraforbando/ ADRPM.pdf. 2. Schrag D. The price tag on progress–chemotherapy for colorectal cancer. N Engl J Med 2004; 351: 317–319. 3. Evans WE, McLeod HL. Pharmacogenomics–drug disposition, drug targets, and side effects. N Engl J Med 2003; 348: 538–549. 4. Freimuth RR, Ameyaw M-M, Pritchard SC et al. High-throughput Genotyping Methods for Pharmacogenomic Studies. Current Pharmacogenomics 2004; 2: 21–33. 5. Syvanen AC. Toward genome-wide SNP genotyping. Nat Genet 2005; 37S: S5–10. 6. Kwok PY. Methods for genotyping single nucleotide polymorphisms. Annu Rev Genomics Hum Genet 2001; 2: 235–258. 7. Innocenti F (ed). Pharmacogenomics Methods and Applications. Humana Press 2005. 8. McLeod HL, Marsh S. Pharmacogenetics goes 3D. Nat Genet 2005; 37: 794–795. 9. Lenz HJ. The use and development of germline polymorphisms in clinical oncology. J Clin Oncol 2004; 22: 2519–2521. 10. Yu J, Shannon WD, Watson MA, McLeod HL. Gene expression profiling of the irinotecan pathway in colorectal cancer. Clin Cancer Res 2005; 11: 2053–2062. 11. Cheng Q, Yang W, Raimondi SC et al. Karyotypic abnormalities create discordance of germline genotype and cancer cell phenotypes. Nat Genet 2005; 37: 878–882. 12. Stebbing J, Bower M, Syed N et al. Epigenetics: an emerging technology in the diagnosis and treatment of cancer. Pharmacogenomics 2006; 7: 747–757. 13. Marsh S, Mallon MA, Goodfellow P, McLeod HL. Concordance of pharmacogenetic markers in germline and colorectal tumor DNA. Pharmacogenomics 2005; 6: 873–877. 14. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999; 286: 487–491. 15. Marsh S. Pharmacogenetics of colorectal cancer. Expert Opin Pharmacother 2005; 6: 2607–2616. 16. Hoskins JM, Mcleod HL. Cancer Pharmacogenetics: The Move from Pharmacokinetics to Pharmacodynamics. Current Pharmacogenomics 2006; 4: 39–46.
Volume 18 | Supplement 9 | July 2007
17. Marsh S, Van Booven DJ, McLeod HL. Global pharmacogenetics: giving the genome to the masses. Pharmacogenomics 2006; 7: 625–631. 18. Engen RM, Marsh S, Van Booven DJ, McLeod HL. Ethnic differences in pharmacogenetically relevant genes. Curr Drug Targets 2006; 7: 1641–1648. 19. Marsh S, Kwok P, McLeod HL. SNP databases and pharmacogenetics: great start, but a long way to go. Hum Mutat 2002; 20: 174–179. 20. Bhatti P, Church DM, Rutter JL et al. Candidate single nucleotide polymorphism selection using publicly available tools: a guide for epidemiologists. Am J Epidemiol 2006; 164: 794–804. 21. Altshuler D, Brooks LD, Chakravarti A et al. A haplotype map of the human genome. Nature 2005; 437: 1299–1320. 22. Freimuth RR, Xiao M, Marsh S et al. Polymorphism discovery in 51 chemotherapy pathway genes. Hum Mol Genet 2005; 14: 3595–3603. 23. Paez JG, Janne PA, Lee JC et al. EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy. Science 2004; 304: 1497–1500. 24. Lynch TJ, Bell DW, Sordella R et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 2004; 350: 2129–2139. 25. Pao W, Miller V, Zakowski M et al. EGF receptor gene mutations are common in lung cancers from ‘‘never smokers’’ and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA 2004; 101: 13306–13311. 26. Relling MV, Hancock ML, Rivera GK et al. Mercaptopurine therapy intolerance and heterozygosity at the thiopurine S-methyltransferase gene locus. J Natl Cancer Inst 1999; 91: 2001–2008. 27. McLeod HL, Siva C. The thiopurine S-methyltransferase gene locus—implications for clinical pharmacogenomics. Pharmacogenomics 2002; 3: 89–98. 28. Nackley AG, Shabalina SA, Tchivileva IE et al. Human catechol-Omethyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science 2006; 314: 1930–1933. 29. Wang D, Sadee W. Searching for polymorphisms that affect gene expression and mRNA processing: example ABCB1 (MDR1). Aaps J 2006; 8: E515–520. 30. Viguier J, Boige V, Miquel C et al. ERCC1 Codon 118 Polymorphism Is a Predictive Factor for the Tumor Response to Oxaliplatin/5-Fluorouracil Combination Chemotherapy in Patients with Advanced Colorectal Cancer. Clin Cancer Res 2005; 11: 6212–6217. 31. Stoehlmacher J, Park DJ, Zhang W et al. A multivariate analysis of genomic polymorphisms: prediction of clinical outcome to 5-FU/oxaliplatin combination chemotherapy in refractory colorectal cancer. Br J Cancer 2004; 91: 344–354. 32. Kang S, Ju W, Kim JW et al. Association between excision repair crosscomplementation group 1 polymorphism and clinical outcome of platinum-based chemotherapy in patients with epithelial ovarian cancer. Exp Mol Med 2006; 38: 320–324. 33. Bao L, Zhou M, Wu L et al. PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits. Nucleic Acids Res 2007; 35: D51–54. 34. Wei X, McLeod HL, McMurrough J et al. Molecular basis of the human dihydropyrimidine dehydrogenase deficiency and 5-fluorouracil toxicity. J Clin Invest 1996; 98: 610–615. 35. Van Kuilenburg AB, Meinsma R, Zoetekouw L, Van Gennip AH. High prevalence of the IVS14 + 1G>A mutation in the dihydropyrimidine dehydrogenase gene of patients with severe 5-fluorouracil-associated toxicity. Pharmacogenetics 2002; 12: 555–558. 36. Marsh S. Thymidylate synthase pharmacogenetics. Invest New Drugs 2005; 23: 533–537. 37. Villafranca E, Okruzhnov Y, Dominguez MA et al. Polymorphisms of the repeated sequences in the enhancer region of the thymidylate synthase gene promoter may predict downstaging after preoperative chemoradiation in rectal cancer. J Clin Oncol 2001; 19: 1779–1786. 38. McLeod HL, Tan B, Malyapa R et al. Genotype-guided neoadjuvant therapy for rectal cancer. Proc Am Soc Clin Oncol 2005; 23: 197. 39. Ulrich CM, Bigler J, Velicer CM et al. Searching expressed sequence tag databases: discovery and confirmation of a common polymorphism in the thymidylate synthase gene. Cancer Epidemiol Biomarkers Prev 2000; 9: 1381–1385.
doi:10.1093/annonc/mdm289 | ix27
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
especially in populations with low incidence of UGT1A1*28 [47], may give a clearer picture of toxicity risk. In addition, several other genes could contribute to irinotecan toxicity [48, 49]. For example, genotyping of tagSNPs in the multi-drug transporter ABCC2 may also be of importance to predict patients at risk for irinotecan toxicity [50]. Drug activity and efficacy is unlikely to ever be defined by the actions of a single gene, or a single polymorphism within a gene. Once comprehensive assessment of candidate genes and polymorphisms has been completed, panels of markers will need to be identified for each treatment combination to build a comprehensive pharmacogenomic profile for each patient. Much progress has been made in pharmacogenomics over the past decade, but there is a considerable amount of work remaining before pharmacogenomics is fully integrated into clinical practice.
symposium article
symposium article 40. Mandola MV, Stoehlmacher J, Zhang W et al. A 6 bp polymorphism in the thymidylate synthase gene causes message instability and is associated with decreased intratumoral TS mRNA levels. Pharmacogenetics 2004; 14: 319–327. 41. Bosma PJ, Chowdhury JR, Bakker C et al. The genetic basis of the reduced expression of bilirubin UDP-glucuronosyltransferase 1 in Gilbert’s syndrome. N Engl J Med 1995; 333: 1171–1175. 42. Beutler E, Gelbart T, Demina A. Racial variability in the UDPglucuronosyltransferase 1 (UGT1A1) promoter: a balanced polymorphism for regulation of bilirubin metabolism? Proc Natl Acad Sci USA 1998; 95: 8170–8174. 43. Innocenti F, Undevia SD, Iyer L et al. Genetic variants in the UDPglucuronosyltransferase 1A1 gene predict the risk of severe neutropenia of irinotecan. J Clin Oncol 2004; 22: 1382–1388. 44. Invader UGT1A1 molecular assay for irinotecan toxicity. A genetic test for an increased risk of toxicity from the cancer chemotherapy drug irinotecan(Camptosar). Med Lett Drugs Ther 2006; 48: 39–40.
Annals of Oncology
45. Ratain MJ. From bedside to bench to bedside to clinical practice: an odyssey with irinotecan. Clin Cancer Res 2006; 12: 1658–1660. 46. McLeod HL. Drug pathways: moving beyond single gene pharmacogenetics. Pharmacogenomics 2004; 5: 139–141. 47. Sai K, Saeki M, Saito Y et al. UGT1A1 haplotypes associated with reduced glucuronidation and increased serum bilirubin in irinotecanadministered Japanese patients with cancer. Clin Pharmacol Ther 2004; 75: 501–515. 48. de Jong FA, de Jonge MJ, Verweij J, Mathijssen RH. Role of pharmacogenetics in irinotecan therapy. Cancer Lett 2006; 234: 90–106. 49. Marsh S, McLeod HL. Pharmacogenetics of irinotecan toxicity. Pharmacogenomics 2004; 5: 835–843. 50. de Jong FA, Scott-Horton TJ, Kroetz DL et al. Irinotecan-induced diarrhea: functional significance of the polymorphic ABCC2 transporter protein. Clin Pharmacol Ther 2007; 81: 42–49.
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
ix28 | Marsh
Volume 18 | Supplement 9 | July 2007
Annals of Oncology 18 (Supplement 9): ix24–ix28, 2007 doi:10.1093/annonc/mdm289
Pharmacogenomics S. Marsh Washington University, Division of Molecular Oncology, St Louis, USA
Adverse drug reactions (ADRs) resulting from cancer chemotherapy are estimated to account for 1.9% of overall hospital costs and 15% of overall treatment costs [1]. With multiple drug strategies for many cancer types now approved for use, it is essential to discover markers to identify the most appropriate therapy for each patient. In addition to possible life-threatening ADRs, and the obvious associated trauma and inconvenience for the patient, there is often a narrow window of time for treatment to be effective. Added to this, the cost of some chemotherapy combinations may be prohibitive to some individuals or healthcare providers. Using colorectal cancer as an example, the cost of 5-fluorouracil/leucovorin therapy is approximately 151 times less than the cost of 5-fluorouracil/ leucovorin/irinotecan, and approximately 489 times less than irinotecan/cetuximab [2]. Although the increase in response rate from using combination therapy justifies the expense, the incidence of ADRs and the inter-individual variation in response makes the identification of markers to predict the appropriate therapy combination in advance of treatment selection essential. In addition to environmental and physiological influences, variation in the genetic constitution between individuals will have a major impact on drug activity. Pharmacogenomics encompasses the search for genetic basis for inter-individual differences in drug response [3] with the ultimate goal of personalized therapy selection. Single nucleotide polymorphisms (SNPs) account for the majority of genetic variation in the human genome. The remainder of the variation consists of insertions and deletions (indels), tandem repeats and microsatellites (Table 1). With the completion of the human genome project, there has been a great deal of progress in the discovery, characterization and validation of genetic variation. A variety of genotyping platforms are now available to researchers [4–7], including low throughput e.g. polymerase chain reaction–restriction length polymorphisms (PCR–RFLP) and allele-specific PCR; medium throughput e.g. Taqman, Pyrosequencing, matrixassisted laser desorption ionization time-of-flight (MALDITOF); and high throughput e.g. SNP chips and bead arrays. The availability of resources for fast and accurate polymorphism analysis makes pre-treatment genotyping a realistic possibility.
sources of DNA Pharmacogenomics includes studies of variations in germ-line DNA, somatic mutations and variations in RNA expression [8]. ª 2007 European Society for Medical Oncology
Many studies rely on the availability of DNA and RNA from easily accessible sources, such as blood or saliva [9]. In cancer pharmacogenomics the tumour genome can differ significantly from the germ-line genome. Differences in RNA expression between germ-line and tumour DNA have been documented [10], in addition to regions of chromosome loss, gain and rearrangements, and aberrant methylation [8, 11, 12]. Whilst pharmacogenomic markers in germ-line DNA show high concordance with tumour genotype [13], and markers predictive of toxicity can be usefully assessed in germ-line DNA [9], markers for outcome may not be so well represented in the germ-line genome. Gene amplification and somatic mutations in the tumour genome may be more predictive for outcome to chemotherapy agents than germ-line markers alone [8].
sources of polymorphisms literature There are many published examples of polymorphisms in genes relevant to cancer pharmacogenomics [3, 14–16]. If knowledge of a functional polymorphism in the gene is available (e.g. UGT1A1*28) then this represents an excellent starting point for hypothesis-testing association studies. However, not all genes have been comprehensively screened for polymorphisms and critical information may be missing from the literature for many genes. Often, published pharmacogenomic associations have been performed in small sample sets (<100 patients) and lack validation in large, prospective studies. In addition, significant ethnic variability in genotype frequencies can occur [17, 18], so care should be taken to select polymorphisms from the literature that are known to be present and functional in the representative population(s) of the patient samples to be tested. databases Publicly available polymorphism databases [19] can provide more comprehensive data on variants within genes of interest and therefore they are an essential resource for pharmacogenomic studies, especially where published data is limited. In the May 2006 release (Build 126) of the National Center for Biotechnology Information (NCBI) polymorphism database, dbSNP, there were over 27 million submissions corresponding to over 11 million polymorphisms in the human genome. However, only about half of the polymorphisms in dbSNP have been validated, limiting the number of SNPs that can be confidently included in pharmacogenomic studies without further validation from the investigator [19]. The vast
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
symposium article
introduction
symposium article
Annals of Oncology
Table 1. Glossary of terms
exon flanking region haplotype indel intron linkage disequilibrium methylation
tandem repeat or VNTR untranslated region
amount of data in SNP repositories, coupled with often cumbersome user interfaces can make them difficult to manoeuvre; consequently programs have been developed to allow users to download and manipulate data more easily [20]. More recently, the International HapMap project [21] was initiated to genotype SNPs in multiple world populations (European Caucasian, Chinese, Japanese, Yorubi) to identify regions of linkage disequilibrium and allow haplotype construction across chromosomes and/or gene regions. This data can be utilized in genome-wide association studies to identify novel pharmacogenomic genes. It can also provide a starting point in the form of tagSNPs across a candidate gene where functional polymorphism(s) are currently unknown. To date the HapMap project has deposited over 20 million genotypes from four populations into dbSNP, making this one of the largest publicly available pharmacogenomics resources [21].
resequencing Despite the wealth of knowledge in the literature and public databases, resequencing projects are still identifying many novel variants, especially between different ethnic groups. This suggests that the extent of variation in the human genome is still unknown, and the distribution of those variants among population groups is not fully understood [22]. Consequently, resequencing in diverse ethnic populations is still ongoing in pharmacogenomic studies, and is essential for genes that are currently poorly represented in the databases and literature. In addition, resequencing of the tumour genome can identify somatic mutations that can have a major effect on chemotherapy outcome [23–25]. These mutations are typically not present in the germ-line DNA from patients and consequently would be missed by screening just for previously characterized polymorphisms.
Volume 18 | Supplement 9 | July 2007
location of polymorphisms Functional polymorphisms can be found in any region of a gene; however, the location within the gene will have a correspondingly different functional effect.
exon Polymorphisms within the coding region of a gene, especially non-synonymous polymorphisms, are frequently studied as altering the amino acid sequence is likely to cause a functional difference. Examples of functional non-synonymous polymorphisms include SNPs within the gene encoding thiopurine methyltransferase (TPMT). TPMT methylates 6-mercaptopurine, a drug commonly used in the treatment of childhood acute lymphocytic leukaemia. Reduced TPMT activity is associated with severe haematological toxicity and reduction of mercaptopurine dose is required in patients with low or intermediate TPMT activity [26]. Three common non-synonymous variant alleles (TPMT*2 [amino acid substitution A80P], TPMT*3A [amino acid substitutions A154T and Y240C] and TPMT*3C [amino acid substitution Y240C]) account for up to 95% of low TPMT activity phenotypes [27]. Patients heterozygous for these alleles have intermediate TPMT levels and require approximately 65% of standard mercaptopurine dosage, patients homozygous for the variant TPMT alleles require significant (1/10 to 1/15) mercaptopurine dose reduction [26]. Although synonymous polymorphisms do not alter the amino acid sequence, they can still have a functional effect. They could affect RNA secondary structure [28] or have a role in allelic imbalance and consequently altered gene expression [29]. A synonymous SNP in the DNA excision repair gene, ERCC1, is associated with response to 5-fluorouracil/ oxaliplatin therapy in colorectal cancer patients [30, 31], and response to platinum therapy in ovarian cancer patients [32].
doi:10.1093/annonc/mdm289 | ix25
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
microRNA microsatellite non-synonymous SNP polymorphism resequencing SNP synonymous SNP tagSNP or htSNP
regions of the gene that are translated into amino acid sequence regions upstream and downstream of untranslated regions. Not transcribed into RNA but can contain control element nucleotide sequence along a gene or chromosome region determined by the extent of linkage disequilibrium insertion/deletion: insertion or deletion of one or several bases non-coding regions of a gene. Can be part of alternative spliced forms of the gene non-random inheritance of polymorphisms. A D’ of 1 or –1 represents perfect linkage disequilibrium, a D’ of 0 represents completely random inheritance addition of a methyl group to the cytosine base of CG dinucleotides (CpG islands). Usually occurs in gene promoter regions, leading to transcription silencing short RNA sequences that can regulate gene expression by repression of translation or affecting mRNA stability polymorphic tandem repeats consisting of short stretches of nucleotides (usually 1-4 bases) occurring throughout the genome SNP within an exon that causes an amino acid substitution mutation present in the population at a frequency of at least 1% sequencing the same region of DNA in multiple samples to identify polymorphisms single nucleotide polymorphism: DNA differs between individuals at one base SNP within an exon that does not alter the amino acid sequence marker that provides information for multiple polymorphisms, reducing the amount of genotyping necessary for complete coverage of a gene or chromosome region repeated adjacent sequence, number of bases varies, number of repeats is polymorphic gene region transcribed into RNA but not translated into amino acid sequence
symposium article Although the function of the codon 118 synonymous polymorphism is unclear, this SNP may still be a useful pharmacogenomic marker for predicting response to platinumbased therapy.
5#-untranslated region/flanking region The 5# regions of genes contain regulatory elements (e.g. promoters, enhancer regions) that control gene expression. For example, a polymorphic 28-bp tandem repeat in the 5#untranslated region (5#-UTR) containing a promoter–enhancer region of the thymidylate synthase (TYMS) gene has been identified. This repeat polymorphism (TSER) varies from 2 (TSER*2) to 9 (TSER*9) copies of the repeat, with TSER*2 and TSER*3 being the most common alleles [36]. TSER*3 leads to significantly increased TYMS expression compared with TSER*2 [36]. There have been several clinical studies identifying a role for the TYMS TSER in response to 5-fluorouracil chemotherapy [36]. Patients with rectal cancer were significantly more likely to experience downstaging (a measure of tumour response to treatment) in patients with TSER*2 alleles who were treated with 5-fluorouracil, compared with patients homozygous for TSER*3 [37]. The first genotypeguided clinical cancer trial in North America is currently underway based on TSER genotype. Rectal cancer patients with the TSER*2 allele are treated with standard therapy (5-fluorouracil and radiation). Patients homozygous for TSER*3 are treated with 5-fluorouracil/radiation and an additional chemotherapy agent (irinotecan). Preliminary data suggest an improved response rate for both groups [38]. 3#-untranslated region DNA variations in the 3#-UTR of a gene can have an impact on RNA stability, altering the half-life of RNA and consequently the expression levels of the gene. For example, a 6-bp deletion located in the 3#-UTR, 447 bp downstream from the stop codon, has been identified in TYMS [39]. The deletion has been associated with decreased TYMS mRNA levels in tumour cells, possibly by affecting mRNA stability [40]. The significance of this polymorphism on response to 5-fluorouracil therapy has
ix26 | Marsh
not yet been comprehensively assessed. However, decreasing TYMS mRNA levels in the tumour cell could be hypothesized to lead to an improved response to 5-fluorouracil therapy.
somatic mutations Mutations within the tumour genome could also have pharmacogenomic relevance. Resequencing the exons of the epidermal growth factor receptor (EGFR) gene in tumour DNA from non-small cell lung cancer patients who responded favourably to the EGFR inhibitors gefitinib or erlotinib, identified several somatic mutations in the EGFR gene [23–25]. Conversely, no mutations were identified in patients who did not respond to gefitinib or erlotinib [23, 24]. In addition, paired normal tissue from the same patients did not carry the mutations. Consequently, screening of the patients germ-line DNA would not provide useful information for predicting therapy outcome in this instance, screening of the tumour genome would be necessary [23, 24].
pharmacogenomics in clinical practice The integration of pharmacogenomics into clinical practice is now being realized. In addition to genotype-guided clinical trials [38], the US Food and Drug Administration (FDA) has begun the process of approving the use of pharmacogenomic data for dose selection in cancer treatment. A dinucleotide TA repeat in a TATA box in the UGT1A1 promoter results in altered UGT1A1 expression [41]. The variable number of TA repeats ranges from 5 to 8 copies, 6 TA repeats represents the most common allele (wild-type UGT1A1 expression), and the 7 TA repeat (UGT1A1*28) is the most common variant allele (reduced UGT1A1 expression) [42]. Reduced UGT1A1 expression is linked to an increased risk of toxicity from irinotecan therapy, including severe diarrhoea and neutropenia [43]. A prospective study administered irinotecan to 66 patients with advanced disease. UGT1A1*28 genotype and haplotypes containing UGT1A1*28 were correlated with irinotecan pharmacology and the occurrence of severe toxicity [43]. Patients homozygous for UGT1A1*28 had a significantly greater risk of grade IV neutropenia compared with heterozygous or homozygous wild-type patients [43]. In 2005 the FDA approved a genetic test for UGT1A1*28 [44] and included toxicity and dosing warnings relating to the UGT1A1*28 allele in the irinotecan package insert [45], marking a significant step towards the integration of pharmacogenomics into clinical practice.
polygenic pharmacogenomics strategies Although there are several published examples of clinically relevant polymorphisms, it is clear that assessing a single polymorphism in a single gene will not provide all of the information required to accurately select therapy in advance of treatment [46]. Whilst UGT1A1*28 is clearly associated with the incidence of toxicity from irinotecan, not all patients who experience toxicity carry the UGT1A1*28 polymorphism. Genotyping of additional polymorphisms within UGT1A1,
Volume 18 | Supplement 9 | July 2007
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
intron The vast majority of gene polymorphisms are located within the introns. Although this is often labelled ‘‘junk’’ DNA, intronic polymorphisms can be located within regulatory regions, or within putative microRNA target sites, subsequently affecting gene expression [33]. In addition, they may become coding region polymorphisms within alternative spliced versions of the gene. Polymorphisms located within the intron/exon splice junctions can also cause exons to be skipped by abolishing the splice site. A well-characterized example of a functional intronic polymorphism causing exon skipping is the IVS14+1G>A SNP in the gene encoding dihydropyrimidine dehydrogenase (DPYD). This SNP, also known as DPYD*2A, is located at the first nucleotide of intron 14. The polymorphism causes exon 14 to be skipped and results in a protein with reduced activity [34]. Patients carrying DPYD*2A experience severe life-threatening toxicity from the commonly used chemotherapy drug 5-fluorouracil [34, 35].
Annals of Oncology
Annals of Oncology
acknowledgements Comments and suggestions for this manuscript from Derek Van Booven were greatly appreciated. The author is funded in part by the Pharmacogenomics Research Network (U01 GM63340) and R21 CA113491.
references 1. Wiffen P, Gill M, Edwards J, Moore A. Adverse drug reactions in hospital patients Bandolier Extra. 2002 http://www.jr2.ox.ac.uk/bandolier/Extraforbando/ ADRPM.pdf. 2. Schrag D. The price tag on progress–chemotherapy for colorectal cancer. N Engl J Med 2004; 351: 317–319. 3. Evans WE, McLeod HL. Pharmacogenomics–drug disposition, drug targets, and side effects. N Engl J Med 2003; 348: 538–549. 4. Freimuth RR, Ameyaw M-M, Pritchard SC et al. High-throughput Genotyping Methods for Pharmacogenomic Studies. Current Pharmacogenomics 2004; 2: 21–33. 5. Syvanen AC. Toward genome-wide SNP genotyping. Nat Genet 2005; 37S: S5–10. 6. Kwok PY. Methods for genotyping single nucleotide polymorphisms. Annu Rev Genomics Hum Genet 2001; 2: 235–258. 7. Innocenti F (ed). Pharmacogenomics Methods and Applications. Humana Press 2005. 8. McLeod HL, Marsh S. Pharmacogenetics goes 3D. Nat Genet 2005; 37: 794–795. 9. Lenz HJ. The use and development of germline polymorphisms in clinical oncology. J Clin Oncol 2004; 22: 2519–2521. 10. Yu J, Shannon WD, Watson MA, McLeod HL. Gene expression profiling of the irinotecan pathway in colorectal cancer. Clin Cancer Res 2005; 11: 2053–2062. 11. Cheng Q, Yang W, Raimondi SC et al. Karyotypic abnormalities create discordance of germline genotype and cancer cell phenotypes. Nat Genet 2005; 37: 878–882. 12. Stebbing J, Bower M, Syed N et al. Epigenetics: an emerging technology in the diagnosis and treatment of cancer. Pharmacogenomics 2006; 7: 747–757. 13. Marsh S, Mallon MA, Goodfellow P, McLeod HL. Concordance of pharmacogenetic markers in germline and colorectal tumor DNA. Pharmacogenomics 2005; 6: 873–877. 14. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999; 286: 487–491. 15. Marsh S. Pharmacogenetics of colorectal cancer. Expert Opin Pharmacother 2005; 6: 2607–2616. 16. Hoskins JM, Mcleod HL. Cancer Pharmacogenetics: The Move from Pharmacokinetics to Pharmacodynamics. Current Pharmacogenomics 2006; 4: 39–46.
Volume 18 | Supplement 9 | July 2007
17. Marsh S, Van Booven DJ, McLeod HL. Global pharmacogenetics: giving the genome to the masses. Pharmacogenomics 2006; 7: 625–631. 18. Engen RM, Marsh S, Van Booven DJ, McLeod HL. Ethnic differences in pharmacogenetically relevant genes. Curr Drug Targets 2006; 7: 1641–1648. 19. Marsh S, Kwok P, McLeod HL. SNP databases and pharmacogenetics: great start, but a long way to go. Hum Mutat 2002; 20: 174–179. 20. Bhatti P, Church DM, Rutter JL et al. Candidate single nucleotide polymorphism selection using publicly available tools: a guide for epidemiologists. Am J Epidemiol 2006; 164: 794–804. 21. Altshuler D, Brooks LD, Chakravarti A et al. A haplotype map of the human genome. Nature 2005; 437: 1299–1320. 22. Freimuth RR, Xiao M, Marsh S et al. Polymorphism discovery in 51 chemotherapy pathway genes. Hum Mol Genet 2005; 14: 3595–3603. 23. Paez JG, Janne PA, Lee JC et al. EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy. Science 2004; 304: 1497–1500. 24. Lynch TJ, Bell DW, Sordella R et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 2004; 350: 2129–2139. 25. Pao W, Miller V, Zakowski M et al. EGF receptor gene mutations are common in lung cancers from ‘‘never smokers’’ and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA 2004; 101: 13306–13311. 26. Relling MV, Hancock ML, Rivera GK et al. Mercaptopurine therapy intolerance and heterozygosity at the thiopurine S-methyltransferase gene locus. J Natl Cancer Inst 1999; 91: 2001–2008. 27. McLeod HL, Siva C. The thiopurine S-methyltransferase gene locus—implications for clinical pharmacogenomics. Pharmacogenomics 2002; 3: 89–98. 28. Nackley AG, Shabalina SA, Tchivileva IE et al. Human catechol-Omethyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science 2006; 314: 1930–1933. 29. Wang D, Sadee W. Searching for polymorphisms that affect gene expression and mRNA processing: example ABCB1 (MDR1). Aaps J 2006; 8: E515–520. 30. Viguier J, Boige V, Miquel C et al. ERCC1 Codon 118 Polymorphism Is a Predictive Factor for the Tumor Response to Oxaliplatin/5-Fluorouracil Combination Chemotherapy in Patients with Advanced Colorectal Cancer. Clin Cancer Res 2005; 11: 6212–6217. 31. Stoehlmacher J, Park DJ, Zhang W et al. A multivariate analysis of genomic polymorphisms: prediction of clinical outcome to 5-FU/oxaliplatin combination chemotherapy in refractory colorectal cancer. Br J Cancer 2004; 91: 344–354. 32. Kang S, Ju W, Kim JW et al. Association between excision repair crosscomplementation group 1 polymorphism and clinical outcome of platinum-based chemotherapy in patients with epithelial ovarian cancer. Exp Mol Med 2006; 38: 320–324. 33. Bao L, Zhou M, Wu L et al. PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits. Nucleic Acids Res 2007; 35: D51–54. 34. Wei X, McLeod HL, McMurrough J et al. Molecular basis of the human dihydropyrimidine dehydrogenase deficiency and 5-fluorouracil toxicity. J Clin Invest 1996; 98: 610–615. 35. Van Kuilenburg AB, Meinsma R, Zoetekouw L, Van Gennip AH. High prevalence of the IVS14 + 1G>A mutation in the dihydropyrimidine dehydrogenase gene of patients with severe 5-fluorouracil-associated toxicity. Pharmacogenetics 2002; 12: 555–558. 36. Marsh S. Thymidylate synthase pharmacogenetics. Invest New Drugs 2005; 23: 533–537. 37. Villafranca E, Okruzhnov Y, Dominguez MA et al. Polymorphisms of the repeated sequences in the enhancer region of the thymidylate synthase gene promoter may predict downstaging after preoperative chemoradiation in rectal cancer. J Clin Oncol 2001; 19: 1779–1786. 38. McLeod HL, Tan B, Malyapa R et al. Genotype-guided neoadjuvant therapy for rectal cancer. Proc Am Soc Clin Oncol 2005; 23: 197. 39. Ulrich CM, Bigler J, Velicer CM et al. Searching expressed sequence tag databases: discovery and confirmation of a common polymorphism in the thymidylate synthase gene. Cancer Epidemiol Biomarkers Prev 2000; 9: 1381–1385.
doi:10.1093/annonc/mdm289 | ix27
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
especially in populations with low incidence of UGT1A1*28 [47], may give a clearer picture of toxicity risk. In addition, several other genes could contribute to irinotecan toxicity [48, 49]. For example, genotyping of tagSNPs in the multi-drug transporter ABCC2 may also be of importance to predict patients at risk for irinotecan toxicity [50]. Drug activity and efficacy is unlikely to ever be defined by the actions of a single gene, or a single polymorphism within a gene. Once comprehensive assessment of candidate genes and polymorphisms has been completed, panels of markers will need to be identified for each treatment combination to build a comprehensive pharmacogenomic profile for each patient. Much progress has been made in pharmacogenomics over the past decade, but there is a considerable amount of work remaining before pharmacogenomics is fully integrated into clinical practice.
symposium article
symposium article 40. Mandola MV, Stoehlmacher J, Zhang W et al. A 6 bp polymorphism in the thymidylate synthase gene causes message instability and is associated with decreased intratumoral TS mRNA levels. Pharmacogenetics 2004; 14: 319–327. 41. Bosma PJ, Chowdhury JR, Bakker C et al. The genetic basis of the reduced expression of bilirubin UDP-glucuronosyltransferase 1 in Gilbert’s syndrome. N Engl J Med 1995; 333: 1171–1175. 42. Beutler E, Gelbart T, Demina A. Racial variability in the UDPglucuronosyltransferase 1 (UGT1A1) promoter: a balanced polymorphism for regulation of bilirubin metabolism? Proc Natl Acad Sci USA 1998; 95: 8170–8174. 43. Innocenti F, Undevia SD, Iyer L et al. Genetic variants in the UDPglucuronosyltransferase 1A1 gene predict the risk of severe neutropenia of irinotecan. J Clin Oncol 2004; 22: 1382–1388. 44. Invader UGT1A1 molecular assay for irinotecan toxicity. A genetic test for an increased risk of toxicity from the cancer chemotherapy drug irinotecan(Camptosar). Med Lett Drugs Ther 2006; 48: 39–40.
Annals of Oncology
45. Ratain MJ. From bedside to bench to bedside to clinical practice: an odyssey with irinotecan. Clin Cancer Res 2006; 12: 1658–1660. 46. McLeod HL. Drug pathways: moving beyond single gene pharmacogenetics. Pharmacogenomics 2004; 5: 139–141. 47. Sai K, Saeki M, Saito Y et al. UGT1A1 haplotypes associated with reduced glucuronidation and increased serum bilirubin in irinotecanadministered Japanese patients with cancer. Clin Pharmacol Ther 2004; 75: 501–515. 48. de Jong FA, de Jonge MJ, Verweij J, Mathijssen RH. Role of pharmacogenetics in irinotecan therapy. Cancer Lett 2006; 234: 90–106. 49. Marsh S, McLeod HL. Pharmacogenetics of irinotecan toxicity. Pharmacogenomics 2004; 5: 835–843. 50. de Jong FA, Scott-Horton TJ, Kroetz DL et al. Irinotecan-induced diarrhea: functional significance of the polymorphic ABCC2 transporter protein. Clin Pharmacol Ther 2007; 81: 42–49.
Downloaded from http://annonc.oxfordjournals.org/ at Purdue University Libraries ADMN on May 21, 2015
ix28 | Marsh
Volume 18 | Supplement 9 | July 2007