Robert B. Diasio
Interindividual differences in drug efficacy and safety are important barriers to optimal treatment with anticancer chemotherapeutic agents. As a general rule, individual variations in drug effects may stem from variability in the pharmacokinetics and/or pharmacodynamics of the therapeutic compound and may have both genetic and nongenetic causes. Nongenetic causes may include differences in age, organ function, concomitant therapy, and drug interactions as well as variability in exposure to foodstuff, tobacco smoke, and other environmental factors among patients.1, 2 Genetic factors however, have been shown to be integral determinants of drug response and are estimated to account for 20 to 95% of the variability in drug disposition and drug effects.3 Pharmacogenetics is the study of the hereditary basis of interindividual variability in drug efficacy and safety resulting from the effects of a single gene or a set of candidate genes.4 Such inherited changes or human genetic variation may include, at a molecular level, single-nucleotide polymorphisms (SNPs) or insertions, deletions, and tandem repeats in candidate genes or genomic regions that can collectively affect the regulation and function of proteins relevant to drug disposition and pharmacodynamic effects. In particular, there is already an extensive list of genes identified through the focused pharmacogenetic approach that encode proteins that alter the pharmacokinetics and pharmacodynamics of drug response through effects on drug-metabolizing enzymes, drug transporters, and drug targets or via genes that influence disease predisposition or progression. In general, the majority of identified genetic polymorphisms have been monogenic with high penetrance; for example, the cytochrome P450 2D6 (CYP2D6) genetic polymorphism has a marked effect on the pharmacokinetic parameters of drugs that are activated or inactivated via oxidative metabolism by this enzyme, and it warrants substantially different doses of these drugs during treatment of poor metabolizers.5, 6, 7, 8, 9
It is noteworthy that the observed marked variability in drug efficacy and safety in the clinic cannot be fully explained by polymorphisms in any single gene. Most drug effects and treatment outcomes are determined by networks of genes and interactions among these genes, including additional multiple low-penetrance genes, which may be more difficult to identify through large population studies. With the completion of the Human Genome Project and advances in technologies such as high-throughput sequencing, DNA and protein microarrays, and bioinformatics, there is a unique opportunity to investigate all forms of variability in drug response using the rapidly emerging field of pharmacogenomics, which is focused more broadly on elucidation of the polygenic determinants of drug efficacy and safety.10, 11, 12 Pharmacogenomics may be particularly valuable for improvement of the efficacy and safety of cancer chemotherapeutic agents, as these agents in general have very narrow therapeutic windows. Ultimately, pharmacogenetics and pharmacogenomics may further pave the way to individualized drug therapy and the abandonment of anticancer drug therapy based on treatment with a single uniform dose. Such advances may customize in the near future the choice of drug dosages and/or the type of prescription and thereby minimize variability in therapeutic response or unexpected toxicity as well as lead to the design of highly targeted and specific treatment options for particular segments of a population.
Currently, a number of genes implicated in pharmacological traits have been identified, and their molecular mechanisms and clinical impact in cancer chemotherapy have been increasingly elucidated. In this chapter, we provide an overview of the general principles of pharmacogenetics and of specific genetic polymorphisms that are pertinent for the clinical pharmacology of specific cancer chemotherapy agents in current medical use. Furthermore, we discuss the future importance of pharmacogenomics in developing individualized anticancer chemotherapeutic strategies.
IMPACT OF GENETIC VARIATION ON CANCER CHEMOTHERAPY
Human genetic variability is a determinant of anticancer drug efficacy and safety. This variability, which is the basis of the disciplines of pharmacogenetics and pharmacogenomics, encompasses an array of different types of DNA sequence modifications as well as individual differences in gene expression and regulation. In the present overview, we focus on the most common form of variation in genetic sequence, known as single-nucleotide polymorphism. A polymorphism is defined as a “Mendelian or monogenic trait that exists in the population in at least two phenotypes (and presumably at least two genotypes), neither of which is rare—that is, neither of which occurs with a frequency of less than 1 to 2%.”13 Such polymorphisms may include nonsynonymous SNPs, which are within the open reading frame of the gene and may result in significant amino acid substitutions in the encoded protein, affecting protein function or quantity. Other SNPs are characterized as synonymous polymorphisms in the coding region of the DNA, or SNPs in promoter and enhancer regions of a gene that affect transcription and gene regulation, and intronic SNPs, which may lead to, splice variants, mRNA.14, 15, 16, 17, 18, 19 In addition to these SNPs, other sources of variation in DNA sequence include insertions, deletions, and gene duplications, all of which contribute to the complex and multifactorial phenotypes of drug efficacy and safety.
Figure 24.1 Factors influencing drug response.
Although virtually all drugs are susceptible to the consequences of genetic variability, the application of pharmacogenetic concepts may be especially important in anticancer chemotherapeutic treatment. Anticancer agents frequently are prodrugs that require enzymatic bioactivation to their cytotoxic active forms, while the active forms of these compounds may also undergo further enzymatic detoxification. In both instances, the involved enzyme systems may exhibit genetic polymorphisms, and therefore small but significant changes in anticancer drug metabolism, distribution, transport, or excretion due to modifications such as decreased production of an altered protein (e.g., an enzyme) or increases in the protein amount can lead to interpatient variability in drug effect. Anticancer agents generally have a relatively narrow therapeutic index. In the current treatment strategy, anticancer agents are administered in “standard” doses to patients. This uniform approach to pharmacotherapy prevents the recognition of interindividual variability in drug metabolism and disposition as part of an individualized drug treatment plan.
Chemotherapeutic drug response is a complex outcome, or phenotype, that is affected by interactions between a network of different genes, including interactions between host and tumor genomes, as most anticancer agents do not selectively target tumor tissue. Genetic polymorphisms in pharmacokinetic pathways may collectively impact drug efficacy and host of tumor toxicity through regulation of drug bioavailability, retention, and efflux and detoxification or metabolism in host or tumor cells. Genetic polymorphisms may further occur in genes that encode drug targets or signal pathways involved in drug response as well as in genes that influence tumor or disease characteristics such as invasiveness and drug resistance. The complexity of variability in human drug response may be additionally affected by differences in the frequencies and types of genetic polymorphisms that are prevalent in ethnically defined populations as well as the specific characteristics of the drugs and disease status, all of which may impact gene function. The various factors that can influence drug response in cancer chemotherapy are presented in Figure 24.1. A major aim of pharmacogenetics and pharmacogenomics is to discern which genetic polymorphisms are important in drug response and how knowledge of this variability can be used in the individualization of drug therapy.
METHODS AND RESEARCH STRATEGIES IN PHARMACOGENETICS
Candidate Gene Approach
The methods used to evaluate pharmacogenetic variability in drug response have evolved over the past several decades to include the candidate gene approach, biochemical or pharmacological pathway approaches, and genomic discovery strategies.20 Initially, pharmacogenetic studies dating from the 1950s relied on clinical observation for the identification of inherited differences in drug effects.21, 22, 23, 24 Such scientific enquiries are often classified as “from phenotype to genotype” and were driven by reports of unusual drug effects in the form of severe drug toxicity or therapeutic failure. For instance, hemolysis was observed subsequent to the administration of a clinically approved antimalarial drug (primaquine) in African-American soldiers during World War II. This observed sensitivity to a drug was later reported to be produced by a deficiency of glucose-6-phosphate dehydrogenase.25, 26 These early studies were the basis for the development of the “candidate gene approach,” wherein the aim is to investigate whether distinct phenotypes observed in pharmacodynamic or pharmacokinetic parameters can be associated with a particular candidate gene and a polymorphic genotype using a hypothesis-based study design. This approach relies on previous knowledge of the pharmacokinetic and/or pharmacodynamic parameters of the drug studied, the disease pathophysiology, and the biochemical roles of proteins encoded by particular genes. This prior knowledge facilitates the rational selection and investigation of the clinical relevance of genetic polymorphisms harbored within a given set of candidate genes that may influence the drug response. To date, this methodology has been useful in the identification of numerous SNPs in mainly high-penetrance genes generally encoding drug-metabolizing enzymes, drug transporters, or drug targets producing distinct and easily recognized polymorphic phenotypes in drug response.14, 16, 27, 28 SNPs identified through the candidate gene approach have been shown to have a significant effect on drug efficacy and toxicity (e.g., CYP2D6 and TPMT), and in rare cases this information has been used to predict anticancer drug response.29, 30, 31
The candidate gene approach showed considerable clinical benefit in the identification of monogenic SNPs influencing drug metabolism and bioavailability. Antineoplastic agents characteristically are administered at maximally tolerated doses, and plasma concentrations often correlate with efficacy and/or toxicity. Thus, a wide variation in plasma concentration-time course (pharmacokinetics) between individuals can lead to adverse drug events and unpredictability in drug efficacy. An example of the importance of variability in drug pharmacokinetics affecting drug response is the cyclin-dependent kinase inhibitor flavopiridol: the risk of dose-limiting diarrhea is increased in cancer patients with a poor drug glucuronidation phenotype characterized by a low metabolic ratio of flavopiridol glucuronide to flavopiridol.32
A knowledge of the mechanisms governing individual differences in pharmacokinetics may allow dose modification before initiation of pharmacotherapy to achieve optimal drug concentrations and therapeutic effects. Because most human cancers may progress in a relatively short time frame, rapid optimization of drug therapy by genetic or phenotypic tests may be crucial to reduce the long-term morbidity and mortality in treatment of patients with cancer. Genetic polymorphisms in drug-metabolizing enzymes are of particular clinical relevance in explaining pharmacokinetic variability if (1) the polymorphism predicts enzyme activity, (2) the particular enzyme activity predicts drug plasma concentration, and (3) the drug plasma concentration predicts drug efficacy or toxicity.
Pharmacokinetics-Based Individualization of Drug Therapy in the Clinic
The presence of a genetic polymorphism that may affect a particular drug response is often detected by measuring the levels of drug or key metabolite(s) in patients with drug toxicity, treatment failure, or a marked favorable response. However, one may miss excessively elevated drug or metabolite levels if the plasma (serum), urine, or tissue samples are not appropriately timed in relation to the drug administration. A more useful approach for the identification of pharmacogenetic syndromes is to administer a known dose of a drug under controlled testing conditions such that the concentration-time course (pharmacokinetics) of the drug or key metabolite(s) in plasma (serum), urine, or tissue samples, or a combination of the three, can be established. With particular cancer chemotherapeutic agents, where there is a suspect clinically relevant risk of serious toxicity, a test dose may be used prospectively to forecast and lessen the safety risk. Another particularly useful approach is to administer a radioactive tracer dose of a drug to permit accurate quantification of the levels of the drug and various metabolites over time. This method has been used in the identification and characterization of pharmacogenetic syndromes affecting 5-fluorouracil metabolism.33
Genotypic or Phenotypic Tests and the Individualization of Drug Therapy
Once a particular pharmacogenetic syndrome has been identified, pharmacokinetic analysis may be of limited clinical feasibility due to its labor-intensiveness. In addition, observation of changes in drug concentration alone may not allow an understanding of the metabolic pathways responsible for drug metabolism. Therefore, genotypic or phenotypic tests can be more beneficial than pharmacokinetic studies in therapeutic drug monitoring and the individualization of drug therapy. Its advantages over pharmacokinetic analysis are summarized below:
· Genotypic and phenotypic tests are less invasive, as they require a single blood, plasma, serum, urine, or tissue sample for assessment of polymorphisms that may affect drug pharmacokinetics and response.
· Genotypic or phenotypic tests can help predict drug response and toxicity. Drug treatment may be altered to prevent potentially lethal toxicity or lack of efficacy due to genetic polymorphisms in genes that influence drug effects.
· The genotypic or phenotypic profile may also be informative in combination treatment with other therapeutic agents.
· The genotypic or phenotypic profile may be applicable to the individual patient's treatment with a drug in the same or a different drug class (e.g., metabolized through the same polymorphic drug-metabolizing enzyme).
· The genotypic or phenotypic test may provide insight into pharmacodynamic variability and the mechanistic basis for variability in drug response or may help in the identification of patients possibly at risk for rapid disease progression or tumor invasiveness.
The choice of a genetic versus a phenotypic test (predictor) of drug effects is usually associated with specific and/or overlapping advantages and disadvantages, summarized in Table 24.1.
Family Studies (Pattern of Inheritance of Drug Effects)
Cancer chemotherapy drugs are too toxic for the family study approach to assessing patterns of inheritance of drug response used for drugs from other therapeutic classes.34 Other phenotypic tests such as assessment of the activity of a drug-metabolizing enzyme or determination of substrate levels in family members can define the pattern of inheritance.
Once a pharmacokinetic or pharmacodynamic alteration has been defined in an individual patient, studies in the family can provide insight into whether the genetic polymorphism is an autosomal or sex-linked trait and whether the pattern of inheritance is dominant, codominant, or recessive.
With availability of data from the human genome project and the functional assignment of enzyme activity to a particular gene, it is becoming possible to delineate the inheritance of a specific gene. Techniques that can identify a specific DNA sequence or SNP, such as allele-specific polymerase chain reaction-based (PCR-based) methods, should make this technically straightforward.35
Population studies are aimed at the assessment of the frequency of the pharmacogenetic syndrome within the general population as well as frequency differences between populations. As with family studies, population studies can use either phenotypic or genotypic tests. The frequency of individuals with particular phenotypic characteristics can be estimated using the Hardy-Weinberg equation.36 Population studies to assess the specific phenotype or activity of a particular protein may show unimodal or bimodal patterns of distribution. A unimodal distribution in the activity of a particular enzyme, such as a Gaussian or normal distribution pattern, suggests mutations or genetic alterations that lead to a range of activities in the population studied. In contrast, bimodal distributions may indicate mutations that lead to reduced or greater than normal activity in a subset of the population.37 The evaluation of particular phenotypes in population studies related to chemotherapeutic drug metabolism may require assessments using a safer probe drug that is metabolized through the same potential polymorphic drug-metabolizing enzyme but without posing a risk of toxicity to the healthy individual participating in the study. Results from a population study comparing cancer patients to healthy volunteers indicated that cancer stage may also influence the phenotype of an important drug-metabolizing enzyme CYP2C19, for which genotype normally predicts phenotype in the healthy population.38, 39 In this study, patients with advanced cancer had the extensive metabolizer genotype; however, 25% of the patients displayed a poor metabolizer phenotype. This discordance between phenotype and genotype and the decreased activity of CYP2C19 observed in terminally ill cancer patients may influence the clinical efficacy and toxicity of therapeutic agents (e.g. cyclophosphamide) and should be investigated with respect to other drug-metabolizing enzymes.39 The characterization of genes and related phenotypes involved in anticancer drug pharmacokinetic or pharmacodynamic variability or those involved in cancer predisposition may be limited to investigations using individuals with the particular cancer or those already being treated with a particular chemotherapeutic agent.
TABLE 24.1 CHARACTERISTICS OF PHENOTYPIC VERSUS GENOTYPIC PREDICTORS OF DRUG RESPONSE
Some pharmacogenetic syndromes are associated with multiple alleles (see “Pathway Approach” later in the chapter). Thus, population studies provide the relative frequency of the various alleles associated with a specific pharmacological syndrome.
After an assessment of phenotypic and genotypic markers in the general population, it may be useful to undertake surveys in other populations, including patients affected with specific types of cancer or being treated with a particular chemotherapeutic agent. Such studies may define the frequency of the pharmacogenetic syndrome in the cancer patient population at risk. Other demographic factors, such as race, age, and gender, may influence the risk to specific groups.
The methodologies discussed above have had specific applications in the identification of SNPs in single genes using the candidate gene approach. The genetic polymorphisms in single genes have not thus far explained a large proportion of the individual variability in drug response, mainly because most drug response phenotypes are thought to be determined by the interactions among multiple genes as well as with the environment. Therefore, the candidate gene strategy has limited value for identifying polygenic determinants of variability in drug response. On the other hand, this approach may offer an advantage for testing “specific” biological or pharmacological hypotheses in a given and often limited clinical sample of patients.
Genome-wide studies require larger numbers of subjects to achieve statistical power to identify multiple low-penetrance genes, a requirement seldom fulfilled in small heterogeneous patient samples.
The complexity inherent in the genetic determination of drug response warrants the use of methodologies that can evaluate the contribution of polymorphisms in several genes, which may interact in additive, synergistic, or antagonistic ways to influence a single drug response. The biological pathway approach to the analysis of pharmacogenetic variation allows for the investigation of associations between genetic polymorphisms within a particular biological pathway of relevance to the pharmacokinetics or pharmacodynamics of a chemotherapeutic agent and is hypothesis-driven.20 Similar to the candidate gene approach, this approach requires an understanding of the biochemical pathways and specific molecular and signaling interactions involved in these pathways. Pharmacogenetics, however, not only is concerned with the effects of genes on drug response but also considers the effects of drugs on gene function and the interactions between genes that are caused by drugs (pharmacological pathways). This has led to the elucidation of the targets that drugs act on and also of the signaling pathways and of polymorphisms in key rate-limiting steps within these pathways that may be involved in the mechanism of action of drugs. Through investigations of the effects of drugs on genes, one may be able to discern the putative drug targets and the related biological pathways. Polymorphisms in such pathways may in turn influence drug efficacy or resistance to anticancer agents.
Pharmacogenomics is the study of variation in drug response utilizing genome-wide approaches. Methodologies include gene expression arrays and proteomics assays, which are designed to assess differences in gene and protein expression profiles.20 These techniques are being widely applied to define profiles associated with drug response, tumor response, or disease predisposition. Such studies may provide insight into mechanisms for severe toxicity in subsets of patients which, if unexplained, could lead to the termination of development of an agent that might be effective in some patient subsets. These techniques are associated with false-positive and false-negative results. Therefore, subsequent to the identification of a gene or protein potentially implicated in drug response or disease outcome using genomic approaches, a hypothesis-based follow-up research strategy is required (e.g., biochemical functional analysis, large-scale epidemiological studies).
EXAMPLES OF GENETIC VARIATIONS THAT INFLUENCE ANTICANCER CHEMOTHERAPEUTIC DRUG EFFICACY AND SAFETY IN HUMANS
Several pharmacogenetic syndromes are associated with certain cancer chemotherapy agents.36, 40,41,42,43,44,45,46 Table 24.2 and Table 24.3 list the best-characterized polymorphic enzymes and proteins relevant to cancer chemotherapy.
Thiopurine S-Methyltransferase and 6-Mercaptopurine (6-MP)
The purine antimetabolite 6-mercaptopurine (6-MP) is commonly used in the treatment of childhood acute lymphoblastic leukemia (ALL).47 This inactive prodrug requires metabolism through an enzymatic pathway involving hypoxanthine phosphoribosyl transferase, leading to the formation of thioguanine nucleotides (TGN), which can be incorporated into DNA to produce antineoplastic activity. Alternatively, 6-MP may be inactivated through oxidation by xanthine oxidase or may produce anticancer activity through its S-methylation by thiopurine S-methyl transferase (TPMT, E.C. 2.1.167).42, 47 Methylation via TPMT results in the formation of 6-methylmercaptopurine nucleotide, which mediates anticancer activity through its inhibition of phosphoribosyl diphosphate amidotransferase, an enzyme involved in de novo purine synthesis.48 The activation via TPMT has been shown to be the more clinically relevant pathway affecting 6-MP efficacy in the treatment of chronic lymphocytic leukemia; hematological toxicity is due to the negligible detoxifying xanthine oxidase expression in hematological tissues.11 Genetic polymorphisms in the TPMT gene have been associated with considerable variability in both the efficacy and toxicity of 6-MP in childhood ALL.
TABLE 24.2 POLYMORPHISMS IN DRUG-METABOLIZING ENZYMES
TABLE 24.3 POLYMORPHISMS IN DRUG TARGETS OR GENES AFFECTING DRUG RESISTANCE
TPMT Genetic Polymorphisms
Genetic polymorphisms are the molecular basis of variability in TPMT activity49, 50 in most patients. To date, nine TPMT alleles have been associated with decreased TPMT activity,50 with single nucleotide polymorphisms resulting in amino acid substitutions (TPMT*2, TPMT*3A, TPMT*3B, TPMT*3C, TPMT*3D, TPMT*5, and TPMT*6), a premature stop codon (TPMT*3D), and destruction of the splice site between intron 9 and exon 10 (TPMT*4). In addition to the alleles that confer decreased enzymatic activity, a single nucleotide polymorphism has been identified that leads to increased TPMT activity (TPMT*1A). Of these identified alleles, three (TPMT*2, TPMT*3A, and TPMT*3C) are responsible for approximately 95% of the intermediate or low enzyme activity observed in patients.47, 51, 52, 53, 54 Furthermore, the decrease in enzymatic activity associated with these three alleles is due to characteristic effects of each specific single nucleotide polymorphism on the activity of the expressed protein as well as rapid proteolysis of the variant proteins compared to the wild-type protein.55, 56
Pattern-of-Inheritance and Population Studies
TPMT genotype is responsible for considerable interindividual and interethnic differences in phenotype. Patients with one wild-type allele and one variant allele have intermediate activity compared to those who are homozygous for the wild-type allele. Patients with two variant alleles lack significant TPMT activity (are TPMT-deficient).51, 52, 57 Approximately 10% of the general population have intermediate TPMT activity, and 0.3% have low or insignificant enzyme activity.51,52 As a result of the multiple variant TPMT alleles, each associated with variable TPMT enzyme activity, allele-specific PCR or PCR-RFLP has been utilized to detect alleles associated with variability in enzyme activity in different ethnic groups. In the Caucasian population, TPMT*3A is the most common variant allele (3.2 to 5.7% of TPMT alleles), while TPMT*3C has an allele frequency of 0.2 to 0.8% and TPMT*2 a frequency of 0.2 to 0.5%.47, 51 For African and Asian populations however, the TPMT*3C allele has been the only variant allele identified in published studies to date.47, 58, 59
TPMT genetic polymorphisms are linked to variability in the efficacy and toxicity of 6-MP. This variability is of considerable clinical importance due to the large fraction of the general population that is heterozygous for the mutant TPMT alleles (5 to 10%), leading to decreased TPMT enzymatic activity. If the TPMT activity is relatively low, more 6-MP is available. However, pharmacokinetic studies have demonstrated that this may not be reflected in the plasma concentrations of 6-MP.60 Red blood cell TGN concentrations are a better predictor; when elevated, they predict toxicity. Low TGN concentrations may predict failure to respond and could provide a rationale for increasing the dose of 6-MP to individualize therapy.61
Among the subset of ALL patients who were intolerant to 6-MP therapy (i.e., they developed hematopoietic toxicity, hepatotoxicity, mucositis, and/or other toxicities requiring dose reductions or delays in subsequent chemotherapy cycles), sixfold overrepresentation of TPMT deficients and heterozygotes was observed.62 Looking at hematopoietic toxicity alone, this overrepresentation was even more significant; in particular, 71% of patients who experienced hematopoietic toxicity were deficient or heterozygous for the TPMT variant alleles, compared to the general population of ALL patients, among whom 8 to 10% are TPMT homozygous mutant or heterozygous.62 It was further indicated that once TPMT deficiency or heterozygosity was determined in these ALL patients and an appropriate 6-MP dose reduction was made for subsequent therapy, patients were able to undergo 6-MP treatment without acute dose-limiting toxicity and were able to maintain red blood cell TGN levels similar to or greater than those achieved by full doses in homozygous wild-type patients.51, 62, 63, 64 These data provide strong evidence that the diagnosis of TPMT deficiency or heterozygosity before initiation of 6-MP treatment may help to increase the efficacy of 6-MP, limit toxicity, and increase the event-free survival of ALL patients.11
Dihydropyrimidine Dehydrogenase and 5-Fluorouracil (5-FU)
Fluoropyrimidines constitute an important class of antineoplastic agents, with the prototype agent 5-fluorouracil (5-FU) being one of the most commonly prescribed anticancer drugs.65, 66 This agent is used particularly in the treatment of solid tumors of the breast, head, neck, and gastrointestinal tract.67, 68, 69,70 The oral fluoropyrimidine prodrug capecitabine is also being widely tested in various types of cancer.71 The biological activity of 5-FU is mediated through activation or anabolism of the parent prodrug to 5-fluoro-2-deoxyuridine monophosphate (5-FdUMP), which subsequently inhibits thymidylate synthase (TS), an enzyme required for de novo pyrimidine synthesis. On the other hand, 80 to 90% of the administered intravenous 5-FU dose is inactivated through catabolism in the liver, where the rate-limiting enzyme is dihydropyrimidine dehydrogenase (DPD, EC18.104.22.168).33, 72
Catabolism has been shown to be important in 5-FU response. In particular, DPD exhibits a wide interindividual variation in activity of up to 20-fold, and patients with low or negligible DPD activity are unable to efficiently inactivate 5-FU, leading to decreased catabolism, which can produce severe 5-FdUMP–mediated gastrointestinal, hematopoietic, and neurological toxicities.46, 73, 74, 75, 76
DPD Genetic Polymorphisms
Genetic polymorphisms in the DPYD ene lead to complete or partial loss of DPD enzyme activity and thus may be responsible for the considerable patient-to-patient variability in therapeutic efficacy and toxicity.73, 76, 77, 78 The syndrome of DPD deficiency, resulting from molecular defects in the gene coding for DPD (DPYD), leads to complete or partial loss of DPD enzyme activity.79
More than 30 sequence variations in the DPYD gene have been identified, producing multiple complex heterozygote genotypes that are inherited in an autosomal codominant fashion.80 Analyses of the prevalence of the specific variant alleles have shown that the most common inactivating allele (DPYD*2A) is characterized by a G to A transition at the invariant GT splice donor site flanking exon 14 of the DPYD gene.73 This mutation leads to truncated mRNA due to skipping of exon 14, which results in a nonfunctional protein.73, 74, 81 A second single nucleotide polymorphism associated with DPD deficiency is DPYD*13, which is characterized by a T to G transition at a domain important to enzyme activity.82 Familial studies have indicated that DPD deficiency is inherited in an autosomal codominant fashion and that DPD deficiency most likely results from multiple mutations at a single gene locus. For instance, a profoundly deficient patient with heterozygous mutations for both DPYD*13 and DPYD*2A and a spouse with normal DPD activity had two partially deficient offspring (one child being heterozygous for the DPYD*2A variant allele and one child being heterozygous for the DPYD*13 variant allele).82 To date, however, the identified DPYDvariant alleles do not explain all observed cases of DPD deficiency, as many patients with severe 5-FU toxicity have no detected mutations in the DPYD gene.83Further studies are thus warranted to fully elucidate the complex molecular and genetic mechanisms leading to DPD deficiency and 5-FU toxicity.
Pattern-of-Inheritance and Population Studies
Partial and complete DPD deficiencies occur in approximately 0.1% and 3 to 5% of the general population, respectively.77 The high prevalence of this syndrome suggests that DPD deficiency may predispose a significant segment of the general population to an increased risk of altered 5-FU pharmacokinetics and toxicity. However, the complexity of this syndrome, along with the multiple heterozygous genotypes observed in population studies, may limit the usefulness of genotyping for a single mutation in this gene.84 Previous reports of sequence variations in this gene have not consistently predicted DPD enzyme activity and identified patients at risk for 5-FU–mediated toxicity due to DPD deficiency.83, 85, 86 This suggests that, in addition to the investigation of variations in the DPYD gene, further investigations should explore other markers that may act alone or together with DPD to produce 5-FU toxicity. Measurement of DPD activity may be more beneficial than genotypic tests in screening for DPD deficiency. Several genotypic and phenotypic methods, including high-performance liquid chromatography (semiautomated radioassay), mass spectrometry, thin-layer chromatography, and denaturing high-performance liquid chromatography, have been developed to identify DPD deficiency in cancer patients.87, 88, 89, 90 Unfortunately, all of these methods remain too complicated and time consuming for routine clinical use and are unavailable in most treatment centers. Due to the complexity of DPD deficiency and its suggested importance in a significant portion of patients with unpredictable severe 5-FU toxicity, rapid screening tests are needed to assess DPD activity in cancer patients prior to treatment with fluoropyrimidine chemotherapeutic agents. A simple 2-13 C uracil breath test (UraBT) has shown potential for rapidly assessing DPD activity in healthy individuals as well as cancer patients. This test is based on the metabolism of 2-13C uracil by the enzymes of the pyrimidine-catabolic pathway to produce 13CO2 the 13CO2 profiles of individuals with normal DPD activity and deficient DPD activity are distinct.91 The clinical utility of this test is still requires further investigation.
DPD has been identified as having a pivotal role in the modulation of 5-FU plasma concentrations. Studies with thymine and uracil (both endogenous substrates for DPD) and 5-FU have shown that all three substrates have similar affinities for DPD and that 5-FU degradation is inhibited by both thymine and uracil, leading to altered 5-FU pharmacokinetics and, in turn, increased toxicity.92, 93 Furthermore, the inability to degrade 5-FU in patients with decreased DPD activity is associated with an increased risk for the development of 5-FU–related severe toxicity.94 Pharmacokinetic studies in patients receiving 5-FU by continuous infusion have demonstrated that plasma 5-FU levels have a circadian variation. This circadian variation was further shown to inversely correlate with the circadian variation in DPD activity from peripheral blood mononuclear cells, suggesting that plasma 5-FU levels are regulated by DPD.95 The determination of variability in DPD activity could aid in the prediction of altered 5-FU plasma concentrations, which in turn may allow for the development of individualized 5-FU–based pharmacotherapy strategies.
There are marked interindividual differences in response, survival, and toxicity among patients treated with 5-FU. For example, patients with metastatic colorectal cancer have been shown to exhibit an overall response rate of 26% to 5-FU treatment.96 DPD activity has been related to 5-FU efficacy and toxicity.33, 97, 98 Interestingly, DPD also appears to serve a critical role in tumor response to 5-FU, with low intratumor expression of the DPD gene (DPYD) shown to predict favorable response to this agent and increased survival time in patients with colorectal cancer.97
5-FU toxicity is common; 31 to 34% of patients with colorectal cancer treated with 5-FU displayed dose-limiting grade 3 to grade 4 hematological toxicity.99DPD deficiency accounts for 43 to 60% of patients with severe toxicity to 5-FU (in some cases severe enough to result in death).100, 101 The other determinants of 5-FU toxicity are undefined.
UDP-Glucuronosyl-Transferase 1A1 and Irinotecan
Irinotecan (CPT-11), a synthetic analog of camptothecin, is commonly used in the treatment of several solid tumors, including advanced colorectal cancer. This prodrug requires metabolic activation to the active metabolite 7-ethyl- 10-hydroxycamptothecin (SN-38) by carboxylesterase 2, with its antineoplastic activity resulting from the inhibition of topoisomerase I.102 The active metabolite, SN-38, is further glucuronidated via hepatic UDP-glucuronosyl-transferase 1A1 (UGT1A1) to the more polar and inactive metabolite SN-38-glucuronide, which can undergo elimination via the bile and urine.103 CPT-11 may also undergo phase I oxidative metabolism through CYP3A4 and CYP3A5. This oxidative metabolism predominantly leads to inactive metabolites and decreases the fraction of administered CPT-11 available for activation to SN-38.104 Optimum treatment with CPT-11 is hindered by severe dose-limiting diarrhea and neutropenia, which are associated with decreased UGT1A1-mediated inactivation of SN-38.103, 105 Accordingly, the main focus of pharmacogenetic studies has been to elucidate the mechanisms of CPT-11–mediated toxicity, including the variability in the metabolism of CPT-11 and genetic polymorphisms associated with UGT1A1-mediated glucuronidation.
UGT1A1 genetic polymorphisms
The polymorphic phase II detoxification enzyme UGT1A1 is responsible for the inactivation through glucuronidation of a variety of endogenous (bilirubin) and exogenous substrates. Over 30 genetic variants have been identified for UGT1A1, a large fraction of which influence the expression and function of the enzyme.106, 107 The commonly observed Gilbert's syndrome is characterized by mild unconjugated hyperbilirubinemia and is associated with homozygosity for the dinucleotide (TA) insertion in the (TA)6TAA element in the UGT1A1 promoter region108 in the Caucasian population. This polymorphism results in the UGT1A1*28 variant allele, which has seven TA repeats, in contrast to six repeats observed in the wild-type UGT1A1*1 allele. Functionally, the variant allele leads to a 70% decrease in UGT1A1 gene expression compared to the UGT1A1*1 allele.109, 110, 111 The UGT1A1*28 allele is less frequent in Asian populations than in Caucasian populations. In fact, missense mutations are more frequently observed in the Asian population, including mutations in the first exon (UGT1A1* 6 and UGT1A1*27).112
Pattern-of-Inheritance and Population Studies
The frequency of the UGT1A1*28 allele is approximately 35% in Caucasians and African Americans, while it is present at a much lower frequency in Asian populations. This variable prevalence of the UGT1A1*28 allele based on ethnic origin is in part responsible for the wide range in frequency reported for the homozygous UGT1A1*28 genotype in different populations (0.5 to 23%).106, 111, 113 Interestingly, other missense mutations in the coding region of UGT1A1are thought to be responsible for cases of Gilbert's syndrome observed in Asian populations. Particularly, the most common variant present in Asian populations is the UGT1A1*6 allele, which has an allelic frequency from 13 to 23%.114, 115 This mutation is of functional significance and results in a 30% and 60% decrease in bilirubin-glucuronidation for heterozygotes and homozygotes, respectively. These data suggest that, regardless of mutation type, individuals with Gilbert's syndrome may be at increased risk for CPT-11–mediated toxicity. Further studies are required to assess the clinical importance of these mutations as well as polymorphisms in other genes affecting both the efficacy and safety of CPT-11 in various populations.
The suggested cause of CPT-11–induced severe diarrhea is direct enteric injury due to biliary excretion of the active metabolite SN-38 in the absence of adequate glucuronidation.116 Clinical data have shown an inverse relationship between SN-38 glucuronidation rates and severity of diarrhea in patients treated with escalating doses of CPT-11 (ref.103).
Particular genetic polymorphisms in the UGT1A1 gene have been implicated in the occurrence of CPT-11–induced toxicity. The UGT1A1*28 mutation was associated with grade 4 leukopenia and grade 3 to grade 4 diarrhea in Japanese cancer patients.112 The frequency of the UGT1A1*28 variant was 3.5-fold higher in patients with severe toxicity than in those with no CPT-11–induced toxicity. The UGT1A1*6 allele frequency was not altered between the two patient groups, while all patients heterozygous for the UGT1A1*27 allele experienced severe drug-induced toxicity. Interestingly, patients with a UGT1A1*28 mutation in combination with either a UGT1A1*6 or UGT1A1*27 allele also had an increased risk of severe toxicity. In another study, the ratios of SN-38 glucuronide to SN-38 were significantly lower in UGT1A1*28 carriers than in homozygous carries of the UGT1A1*1 allele.105 As a result of the high prevalence of the UGT1A1*28 polymorphism, especially in African American and Caucasian populations, further studies are required to determine whether dose adjustment based on UGT1A1 genotype would have beneficial or adverse effects on the response to CPT-11.
Glutathione-dependent detoxification of reactive electrophiles, including cytotoxic chemotherapeutic agents, is catalyzed by the glutathione S-transferase (GST) superfamily of isozymes, which consists of five subclasses (GSTA1, GSTP1, GSTM1, GSTT1, and GSTZ1).117 Several studies described below have reported associations between GST polymorphisms and the efficacy and/or toxicity of various antineoplastic agents as well as disease outcome.
GST Genetic Polymorphisms
Polymorphisms that occur in the human GST genes may have several effects on protein expression and function; for example, gene duplication can lead to ultrarapid metabolizing phenotypes (e.g., GSTM1*Ax2), and SNPs can result in increased or decreased protein function.118 One such characterized polymorphism in the coding region of GSTP1, for instance, leads to a protein with decreased catalytic activity. Gene deletions may also result in null phenotypes; for instance, patients homozygous for the GSTM1*0 or GSTT1*0 “null” allele, where the GSTM1 or GSTT1 gene has been deleted, cannot express the corresponding GST protein.119, 120, 121, 122
Pattern-of-Inheritance and Population Studies
A significant number of genetic polymorphisms in the GST genes have been identified; however, few have been studied extensively. As a result of the homozygous GSTM1*0 genotype, GSTM1 activity is not present in approximately 50 to 60% of Caucasian and Saudi Arabian populations and 22% of a Nigerian population.123 Similarly, the homozygous GSTT1*0 genotype results in the absence of catalytically active protein.118 Population studies have revealed 64.4% and 9% frequencies for this GSTT1*0 null phenotype in Chinese and Mexican populations, respectively.124
GST polymorphisms have been investigated for their contribution as factors affecting chemotherapeutic response, treatment outcome, and survival in patients with various types of cancer. The GSTM1 and GSTT1 null phenotypes (homozygous GSTM1*0 or GSTT1*0) have been associated with an increased risk of aplastic anemia125 and a number of forms of cancer, including lung, colon, head, neck, and bladder cancer and postmenopausal breast cancer and ovarian cancer.125, 126 Overexpression of GST enzymes is associated with resistance to several cancer chemotherapeutic agents, including chlorambucil, carmustine, nitrogen mustard, melphelan, and the active metabolite of cyclophosphamide (phosphoramide mustard), which are all substrates for GST and are inactivated via GSH conjugation.118
GST genetic polymorphisms have not been found to have consistent effects on disease outcome and survival. Ovarian cancer patients with the combination GSTM1 null and GSTT1 null genotypes demonstrated a decreased survival and reduced progression-free interval when compared with patients without these null genotypes.127 Further, the patients with both null genotypes displayed a lack of response to treatment with cisplatin and alkylating agents, compared with a 54% response rate for patients with alternate genotype combinations.127 These data are inconsistent with the suggested biological mechanism in which a lack of detoxification by GST would be hypothesized to produce improved response and survival rates compared with those for patients without the null genotypes. Contrary results were shown for breast cancer patients homozygous for both the GSTM1*0 and GSTT1*0 alleles compared with patients without the null genotypes.128 In this study, patients who had the null genotypes and were treated with cyclophosphamide combination therapy had a smaller risk of recurrence and hazard of death than patients without the null genotypes. Reduced inactivation of cyclophosphamide-generated reactive oxygen species through a lack of GST activity may produce improved antitumor activity and survival rates in breast cancer.128
Conflicting results have also been reported for patients with childhood ALL. In one report, patients with at least two of the genotypes associated with decreased or lack of GST activity (GSTM1*0, GSTT1*0, or GSTP1Val105 homozygous genotypes) had a 3.5-fold increased risk of recurrence compared with patients who did not have these genotypes.129 In contrast to these results, another similar investigation could not demonstrate any effects of the GSTM1*0 and GSTT1*0 alleles on patient survival.130 Given the complexity and high degree of genetic variability observed in the GST family of enzymes, exact relationships between genotype/ phenotype and response to chemotherapy or disease outcome need further clarification. This may be achieved by using rapid, high-throughput methods capable of assessing multiple GST genotypes or by using RNA and GST protein expression techniques. Further, correlative studies investigating plasma pharmacokinetics or GST substrates and GST activity may shed further light on possible clinically relevant applications for the individualization of chemotherapeutic treatment.
Thymidylate Synthase and 5-FU
Like polymorphisms in genes important for drug metabolism, polymorphisms in the genes for drug targets are also important determinants of drug response and disease outcome. Thymidylate synthase (TS) is the main target of 5-FU. The 5-FU metabolite FdUMP produces a stable complex with TS and a methyl cofactor, leading to inhibition of dTMP synthesis and DNA synthesis.45 Variability in response to 5-FU has been linked to several TS gene (TYMS) genetic polymorphisms.131, 132, 133
TS Genetic Polymorphisms
To date, three polymorphisms in the TYMS gene have been identified. A polymorphism within the 5′-promotor enhancer region (TSER) of the TYMS gene consists of tandem repeats of 28 base pairs ranging from two (TSER*2) to nine (TSER*9) copies.134 The role of most of these alleles in TS expression is currently unknown; however, patients homozygous for the TSER*3 genotype have increased intratumor TS messenger RNA levels135 and elevated TS protein levels136 compared with patients with the homozygousTSER*2 genotype.135, 137, 138 Two additional polymorphisms have been identified. The first is a single nucleotide polymorphism within the second repeat of the TSER*3 allele (G→C, 3RG and 3RC alleles). It has been suggested this polymorphism affects the level of TS expression by abolishing a USF1-binding site.139 The second polymorphism described is a 6bp deletion located in the 3′UTR, 447 base pairs downstream from the stop codon.140
Pattern-of-Inheritance and Population Studies
The polymorphisms in which there is a double (TSER*2) and triple (TSER*3) tandem repeat of 28 base pairs are observed most frequently in Caucasian populations, with higher repeats (TSER*4, TSER*5, and TSER*9 alleles) mainly found in African populations.141 When Asian populations are considered, the homozygous (TSER*3) genotype is approximately twice as frequent (67%) as in Caucasians (38%). Using RFLP analysis, the frequency of the 3RC allele in different ethnic populations was determined to be 56%, 47%, 28%, and 37% for non-Hispanic Whites, Hispanic Whites, African Americans, and Singapore Chinese, respectively.139 Lastly, the 6–base pair deletion polymorphism displayed frequencies of 41%, 26%, 52%, and 76% in non-Hispanic Whites, Hispanic Whites, African-Americans, and Singapore Chinese, respectively.142, 143
Variability in response to 5-FU has been linked to TS, with drug resistance and poor prognosis associated with overexpression of the gene.144, 145, 146, 147, 148Several studies have linked the double- and triple-repeat allelic variants (TSER*2 and TSER*3) with response to 5-FU. Pullarkat and colleagues (2001) demonstrated that patients homozygous for the TSER*3 allele had a 3.6-fold higher level of TS mRNA than those homozygous for the TSER*2 allele.135 These researchers also found that colorectal cancer patients homozygous for the TSER*2 genotype had a response rate of 50%, compared with 9% for those homozygous for the TSER*3 genotype.135 Moreover, patients with the homozygous TSER*2 genotype had less severe side effects in response to 5-FU than those with the TSER*3 homozygous genotype. In another study, colorectal cancer patients homozygous or heterozygous (TSER*2/TSER*3) for the TSER*2allele displayed a higher probability of pathological downstaging (60%) subsequent to neoadjuvant 5-FU–based chemotherapy than patients' homozygotes for the TSER*3 allele (22%).137 Currently, however, some ambiguity exists regarding the definitions of “good” and “poor” outcome in these studies, and larger trials are needed to elucidate the importance of TSER polymorphisms and other TYMS polymorphisms in determining chemotherapeutic outcome.
With respect to the 3RC variant allele, in a trial with 208 colorectal cancer patients, a 1.3-fold (95% CI, 0.9–1.9) increased risk of colorectal cancer was found in patients with the 3RG allele compared with controls; however, the specific functional significance of this polymorphism is unclear.149 Another study reported a decreased response to 5-FU in patients homozygous for the variant 6–base pair deletion located in the 3′UTR (447 base pairs downstream from the stop codon), with an odds ratio of 2.0 for 5-FU–based chemotherapy.150 Overall, these studies suggest that determination of the TSER genotype may be a clinically useful tool in the prediction of response to 5-FU.
Sulfotransferase 1A1 and Tamoxifen
The triphenylethylene antiestrogenic compound tamoxifen is commonly used for the treatment of hormone-responsive breast cancer as well as for the prevention of breast cancer.151 The parent compound is subject to extensive hepatic metabolism, leading to the major metabolites N-desmethyl-tamoxifen, tamoxifen-N-oxide, and 4-hydroxy-tamoxifen (4-OH-tam). The therapeutic efficacy of this antiestrogen is attributed to the parent compound (tamoxifen), its cytochrome P450–mediated hydroxylated metabolite (4-OH-tam), and 4hydroxy-N-desmethyl-tamoxifen (endoxifen), which is formed through hydroxylation of N-desmethyl-tam by CYP2D6. 4-OH-tam has been shown to be significantly more potent than the parent compound, with a 33-fold increased affinity for human breast cancer estrogen receptors.152 The plasma concentration of endoxifen is dependent on the highly polymorphic CYP2D6 genotype of the patient.153, 154This suggests that polymorphisms within the CYP2D6 gene influencing protein function, as well as the administration of concomitant substrates or inhibitors of CYP2D6, may affect the therapeutic outcome of tamoxifen therapy. However, additional studies are required to characterize the clinical relevance of variability in the CYP2D6 gene as a factor in the response to tamoxifen.
One other important class of enzymes involved in the metabolism of tamoxifen consists of the hepatic phenol sulfotransferases. These enzymes are classified as phase II drug-metabolizing enzymes and are important in the sulfation- mediated metabolism of endogenous substrates (e.g., steroids), the inactivation of xenobiotics, and the activation of procarcinogens. There are six characterized isoforms within the phenol sulfotransferase (SULT) family of enzymes, and among these the SULT1A1 isoform plays a critical role in the trans-selective sulfation of 4-OH-tam.155 A functionally significant genetic polymorphism in the SULT1A1 gene has been identified, and data suggest an association with breast cancer risk and therapeutic response to tamoxifen.156
SULT1A1 Genetic Polymorphism and Pattern of Inheritance
A single nucleotide polymorphism characterized by a G→A transition in codon 213 leading to an Arg to His amino acid substitution (SULT1A1*2) has been described for the SULT1A1 gene.157–159 Population studies have indicated the presence of the variant SULT1A1*2 allele at frequencies of 0.321% and 0.269% in Caucasian and Nigerian populations, respectively.160 Functionally, this polymorphism results in a 10-fold decrease in phenol SULT activity in individuals homozygous for the variant allele (SULT1A1*2), compared with those homozygous for the wild-type allele (SULT1A1*1).159
Considerable interpatient variability is associated with 4-OH-tam plasma and tumor tissue concentrations, ranging from 28 to 69% of those determined for tamoxifen.161 Additionally, variability in 4-OH-tam concentration and response to tamoxifen may be associated with the reported induction of SULT1A mRNA by 4-OH-tam156 as well as with prognosis, as the SULT1A1*2 allele leads to a SULT protein with lower catalytic activity than does the SULT1A1*1 allele.159 With respect to the potential association between the risk of breast cancer and the SULT1A1 genotype, results have been inconsistent. One study in postmenopausal woman reported a significant association between the variant SULT1A1*2 allele and an increased risk for breast cancer.162 In contrast, another study reported no association between the SULT1A1 genotype and risk for breast cancer.156
Nowell and colleagues demonstrated an association between the SULT1A1 polymorphism and breast cancer outcome in women treated with adjuvant tamoxifen. Specifically, women homozygous for the SULT1A1*2 allele displayed a threefold increased risk of death (hazard rate, 2.9; 95% CI, 1.1–7.6) compared with patients with the heterozygous (SULT1A1*2/SULT1A1*1) genotype or the homozygous SULT1A1*1 genotype. Additionally, in patients not treated with tamoxifen, no association was noted between survival and the SULT1A1 polymorphism.163
As the results from current studies do not provide consistent evidence regarding the clinical relevance of the SULT1A1 genotype and its relevance in breast cancer chemotherapeutic treatment, further prospective studies are required to assess the impact of the SULT1A1 genotype on disease outcome and therapeutic outcomes as well as the relevance of other polymorphic enzymes involved in the metabolism of tamoxifen.
Aromatase (CYP19) and Aromatase Inhibitors
Estrogens have an established role in the development of several cancers, including breast carcinogenesis.164 In postmenopausal women, estrogen is associated with increased breast cancer risk.165 While estrogen is primarily produced in the ovaries before menopause, most circulating estrogens in postmenopausal women are synthesized from adrenal androgens in adipose tissue, where the final stage of synthesis is catalyzed by the cytochrome P450 hemoprotein-containing enzyme aromatase (CYP19).166, 167 The abnormal expression of this enzyme has been observed in several human cancers, including breast, uterine, testicular, and adrenal tumors.168–171 Based on these data, the regulation of estrogen synthesis through the inhibition of CYP19 has become a strategy for the prevention or treatment of breast cancer. The first aromatase inhibitor (AI) shown to be beneficial in the treatment of advanced breast cancer was the cytochrome P450 inhibitor aminoglutethimide.172, 173 Unfortunately, this compound lacked specificity, and its unfavorable side-effect profile led to the development of more specific second-generation (e.g., formestane and fadrazole) and third-generation AIs.174, 175 Currently, third-generation clinical agents have more than a 1000-fold increased potency compared with aminoglutethimide176 and are associated with fewer side effects.177 They can be classified into two types according to structure. Specifically, exemestane is a steroidal (type I) AI that binds irreversibly to the androgen-binding site of CYP19, while anastrozole, letrozole, and vorozole are nonsteroidal AIs (type II) that bind to the heme moiety of CYP19.174, 178 These agents have increased efficacy over tamoxifen in the treatment of hormone-dependent recurrent or advanced breast cancers179, 180 and are more effective than tamoxifen in adjuvant and neoadjuvant settings as well as breast cancer prevention.181
CYP19 Genetic Polymorphisms
Causal genetic variations underlying common complex human diseases such as breast cancer can be studied through haplotype-based genetic association. Such studies are based on dividing the human genome into genomic segments (blocks) that show little evidence of historical recombination and low haplotype diversity.182 Because of the high degree of linkage disequilibrium observed among SNPs within these blocks, it has been suggested that ancestral disease variants may be identified through evaluation of the underlying haplotypes.183 Hairman and colleagues (2003) determined that the CYP19 locus contains five blocks of linkage disequilibrium; in addition, one of these blocks covers all of the coding region exons and introns of the CYP19 gene and contains four common haplotypes in the Caucasian population.183 Importantly, it was shown within this block that the 3′UTR t allele (rs10046) uniquely tags the most common haplotype.
Nine prospective studies have reported that circulating levels of several steroid hormones, including estrogens and androgens, are directly related to the risk of breast cancer in postmenopausal women.184 Furthermore, the CYP19 3′UTR t-c SNP (rs10046) has been shown to be most strongly associated with circulating estradiol levels in postmenopausal women, compared with other investigated SNPs in genes coding for enzymes regulating sex hormones, and the CYP19 3′UTR t allele in particular was associated with the highest estradiol levels in this study.185 Further analysis indicated that homozygotes for the 3′UTR c allele had significantly smaller mean estradiol-testosterone ratios than homozygotes for the 3′UTR t185 however, CYP19 SNPs only explained a small percentage of the variance in the estradiol-testosterone ratios in these postmenopausal women. Genetic variability in the CYP19 gene may be responsible for only a marginal increase in risk of breast cancer compared with other genetic variants.185 This conclusion is consistent with the current view that the genetic predisposition to breast cancer is the result of a few high-penetrance genes (e.g., BRCA1 and BRCA2), coupled with multiple as yet unidentified lower penetrance genes.
The presence of a SNP in the 3′UTR of the CYP19 gene may be associated with an improved response to letrozole in the treatment of postmenopausal metastatic breast cancer patients.186 The significance of these early findings, as well as the possible mechanisms involved, requires further study.
ERCC1, XPD, and XRCC1 in Platinum Chemotherapy
Cisplatin, carboplatin, and oxaliplatin are platinum analogs routinely used in the treatment of non–small cell lung carcinoma and ovarian, breast, gastrointestinal, and testicular cancers.187 The cytotoxic mechanism of these agents involves the inhibition of DNA replication through the formation of inter- and intrastrand helix-deforming DNA adducts.188 Enhanced DNA repair is an important mechanism of platinum resistance. The repairosome responsible for nucleotide excision repair (NER) consists of more than 16 gene products, and polymorphisms in particular genes involved in this repair pathway may influence the response to platinum therapy. The relevant genes include the excision repair cross-complementation group 1 gene (ERCC1) and the xeroderma pigmentosum group D gene (XPD) as well as the x-ray cross-complementing gene (XRCC1) involved in the repair of single-strand breaks following base excision repair.189–192
ERCC1, XPD and XRCC1 Genetic Polymorphisms and Patterns of Inheritance
Common polymorphisms within the XPD gene include the nonsynonymous SNP leading to a single amino acid change from lysine to glutamine at codon 751 of the XPD protein, which is implicated in the response to platinum agents,193 and the G→A transition at codon 312 leading to an amino acid change from aspartic acid to asparagine.194 These polymorphisms have a high prevalence in the general population, with allele frequencies of 29% and 42% for the SNP at codon 751 and 312, respectively.194 Similar associations have been made at codon 118 of ERCC1 (118C/T), which is located 42 before the start of the helix-turn-helix sites at exon 4. The nonsynonymous SNP localized at this site is characterized by a nucleotide alteration, AAC to AAT, resulting in asparagine, and it has an allele frequency of 46% in the general population. It is associated with diminished ERCC1 mRNA and protein levels.194, 195 Another common polymorphism in the ERCC1 gene, C8092A, is located in the 3′ untranslated region of the gene, has an allele frequency of 4% in the general population, and may be associated with the risk of adult-onset glioma. The polymorphism affects ERCC1 mRNA stability.194, 196
An XRCC1 gene SNP polymorphism characterized by a G→A transition at codon 399 (Arg399Gln) results in an arginine to glutamine amino acid change, has an allele frequency of 25%, and, like the XPD and ERCC1 polymorphisms, is associated with improved platinum response.194
Polymorphisms in the XPD, ERCC1, and XRCC1 DNA repair genes are related to the therapeutic outcome of platinum-based chemotherapy.45
The Lys751Gln polymorphism in the XPD gene was shown to affect response to fluorouracil/oxaliplatin treatment in a clinical trial with 73 patients diagnosed with metastatic colorectal cancer. Specifically, 24% of patients with the Lys751/Lys751 genotype responded to this combination treatment but only 10% of patients with the Lys751/Gln751 genotype and 10% of patients with the Gln751/Gln751 genotype responded. The median survival for those with the Lys751/ Lys751 genotype was 17.4 months (95% CI, 7.9–26.5), compared with 12.8 months (95% CI, 8.5–25.9) for those with the Lys751/Gln751 genotype and 3.3 months (95% CI, 1.4–6.5) for patients with the Gln751/Gln751 genotype.193 In 31 women with breast cancer, the mechanism for this observed increased response predicted by the Lys751/Lys751 genotype was shown to be a result of a reduced DNA repair capacity.197However, these results were not supported by two subsequent studies that suggested that individuals homozygous for the wild-type allele (Lys751/Lys751) had greater repair capacity than patients with at least two of the variant alleles (Asn312 or Gln751).198, 199 Therefore, even though the mechanism for the predictive value of the polymorphism at codon 751 may not be elucidated at present, this genotype may still be useful in the prediction of the therapeutic response to platinum agents.
Variability in the ERCC1 function has been investigated for its utility in pharmacogenetic approaches aimed at the possible improvement of platinum-based chemotherapeutic response and survival outcomes. High expression of this gene is associated with resistance to platinum-based therapy in human ovarian and gastric tumor specimens.190, 192 In patients with advanced non–small cell lung carcinoma treated with platinum-based chemotherapy, two common ERCCI polymorphisms (118C/T and C8092A) were investigated for their possible association with overall survival.196 In this study, an increased overall survival time of 22.3 months was noted for patients with the C8092/C8092 genotype, compared with 13.4 months for those with the C8092/A8092 or A8092/A8092 genotypes. These data provide initial evidence suggesting that copies of the A8092 allele are associated with poor outcome. Interestingly, no significant association was observed between the 118C/T polymorphism and overall survival; however, further studies with larger sample sizes may be required to assess the influence of this polymorphism.
The XRCC1 gene involved in DNA repair mechanisms has also been investigated for the potential association between its polymorphic variants and response to platinum analogs. Functionally, patients with genotypes containing the 399Gln variant allele displayed decreased DNA repair capacity of the XRCC1 protein compared with individuals homozygous for the wild-type 399Arg genotype.200 In 61 advanced colorectal cancer patients treated with 5-FU and oxaliplatin, 18% of the patients responded to treatment, with 66% having stable disease and 16% having progressive disease. Of the responders, the majority of patients (73%) carried the Arg/Arg genotype, with 66% of the nonresponders having the Arg/Gln or Gln/Gln genotypes.201 These preliminary studies are encouraging; however the relationship between DNA repair capacity and response to platinum chemotherapy still remains to be defined.
Epidermal Growth Factor Receptor Mutations and Gefitinib
The epidermal growth factor receptor (EGFR, erb-B1) is a member of the tyrosine kinase receptor family and is implicated in tumor growth through several mechanisms, including inhibition of apoptosis, cellular proliferation, and promotion of angiogenesis.202 The altered expression and deregulation of this receptor led to the rational development of the reversible EGFR inhibitor gefitinib, which is approved as third-line therapy for non–small cell lung carcinoma.203, 204 Results from initial clinical studies indicated tumor response only in a small subset of patients with chemotherapy-refractory advanced non–small cell carcinoma.205, 206 However, the responding subset (~10%) showed a remarkably rapid and significant response.207 EGFR genetic variability was assessed in primary tumors from patients with non–small cell lung cancer who responded to gefitinib, who had no response to gefitinib, and who had not been exposed to gefitinib. Somatic mutations in the tyrosine kinase domain of the EGFR gene were observed in eight of the nine patients with gefitinib-responsive cancer. These mutations were absent in patients with no response to gefitinib. Mutated receptors appeared to show increased tyrosine kinase activity in response to EGF and enhanced sensitivity to inhibition by gefitinib compared with wild-type receptors.208 These findings illustrate the validity of and potential for optimization of targeted therapies through understanding the molecular basis of response.
OTHER GENETIC POLYMORPHISMS OF POTENTIAL IMPORTANCE IN THE RESPONSE TO CHEMOTHERAPY
Over the years a number of additional genes with characterized polymorphisms have been identified and are being evaluated for their potential contribution to variability in response to different chemotherapeutic agents. Among these, the ATP-binding cassette (ABC) transporters have been established as playing a role in the pharmacokinetics of a large number of anticancer agents, including irinotecan, etoposide, and doxorubicin.209 Preliminary data have also suggested a role for the polymorphic methylenetetrahydrofolate reductase gene (MTHFR) in the predisposition to severe myelotoxicity subsequent to treatment of breast cancer patients with methotrexate; however, larger trials are needed to investigate the role of specific polymorphisms in this gene in the response and toxicity of various chemotherapeutic agents.210 Other polymorphic genes currently considered to influence variability in drug response are various cytochrome P450 enzymes (CYP2D6, CYP2C9, CYP2C19, CYP3A4, CYP3A5, CYP3A7), N-acetyltransferases, aldehyde dehydrogenase, O6-alkyl-guanine alkyl transferase, and NAD(P)H:quinone oxidoreductase.40, 41 A largely ignored area of investigation is the impact of aging on the phenotypes of drug metabolism. It is conceivable that some of the extreme variability in drug tolerance seen in aged cancer patients is related to an accelerated decline in the function of particular allelotypes with age. Despite the known importance of many gene products for pharmacogenetic syndromes associated with drugs from other classes,34, 211, 212 limited clinical evidence indicates that polymorphisms of the relevant genes have a significant clinical impact on currently used cancer chemotherapy drugs. To further characterize these genes, as well as future polymorphic genes that may be implicated in the variable response to new and currently used chemotherapeutic agents, prospective clinical trials with the inclusion of phenotypic/ genotypic correlative components are essential.
Optimum cancer treatment is hindered by the significant interpatient variability in disease outcome and chemotherapeutic efficacy and toxicity. To date, pharmacogenetic studies have mainly involved hypothesis-driven, focused investigations aimed at understanding the genetic basis of this variability through determining the effects of specific polymorphic genes on drug pharmacodynamics and pharmacokinetics and disease outcome. The influence of specific polymorphic genes on drug response is variable, but no single polymorphic change can adequately explain all variations in therapeutic efficacy and safety. With many of these investigations, specific conclusions regarding the clinical relevance of the particular polymorphic gene for drug response require large phase I and II clinical trials, with the assessment of relationships between the specific genotypes and specific clinical outcomes.
Pharmacogenetic and pharmacogenomic approaches may significantly enhance drug efficacy and limit toxicity by leading to an “individualized approach to drug therapy” that theoretically has the potential to allow for the selection of the optimum drug or drug combination and the optimum dosage to maximally benefit a specific population or a specific patient. However, an improved understanding of the genetic basis for interindividual differences in drug response is required. Therapeutic response is a multifaceted entity influenced by the contributions of many genes as well as interactions among genes and the environment. New genomic and proteomic methods have the potential to elucidate mechanisms of variability in drug response and improve cancer diagnosis, predict tumor response to particular drugs, and individualize treatment to increase the efficacy and decrease the toxicity of chemotherapeutic agents.
1. Zevin S, Benowitz NL. Drug interactions with tobacco smoking: an update. Clin Pharmacokinet 1999;36:425–438.
2. Loebstein R, Yonath H, Peleg D, et al. Interindividual variability in sensitivity to warfarin: nature or nurture? Clin Pharmacol Ther 2001;70:159–164.
3. Kalow W, Tang BK, Endrenyi L. Hypothesis: comparisons of inter- and intra-individual variations can substitute for twin studies in drug research: pharmacogenetics in biological perspective. Pharmacogenetics 1998;8:283–289.
4. Kalow W. Pharmacogenetics in biological perspective. Pharmacol Rev 1997;49:369–379.
5. Flockhart DA, Oesterheld JR. Cytochrome P450-mediated drug interactions. Child Adolesc Psychiatr Clin N Am 2000;9:43–76.
6. Lin KM, Anderson D, Poland RE. Ethnicity and psychopharmacology: bridging the gap. Psychiatr Clin North Am 1995;18:635–647.
7. Alfaro CL, Lam YW, Simpson J, et al. CYP2D6 inhibition by fluoxetine, paroxetine, sertraline, and venlafaxine in a crossover study: intraindividual variability and plasma concentration correlations. J Clin Pharmacol 2000;40:58–66.
8. Ozdemir V, Shear NH, Kalow W. What will be the role of pharmacogenetics in evaluating drug safety and minimising adverse effects? Drug Saf 2001;24:75–85.
9. Alvan G, Bertilsson L, Dahl ML, et al. Moving toward genetic profiling in patient care: the scope and rationale of pharmacogenetic/ ecogenetic investigation. Drug Metab Dispos 2001;29(4 Pt 2): 580–585.
10. Goldstein DB, Tate SK, Sisodiya SM. Pharmacogenetics goes genomic. Nat Rev Genet 2003;4:937–947.
11. Evans WE. Pharmacogenomics: marshalling the human genome to individualise drug therapy. Gut 2003;52(Suppl 2):10–18.
12. Kalow W. Pharmacogenetics and personalised medicine. Fundam Clin Pharmacol 2002;16:337–342.
13. Vogel FM, Motulsky AG. Human Genetics. Problems and Approaches. Berlin: Springer-Verlag, 1986:498–544.
14. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999;286: 487–491.
15. Meyer UA, Zanger UM. Molecular mechanisms of genetic polymorphisms of drug metabolism. Annu Rev Pharmacol Toxicol 1997;37:269–296.
16. Evans WE, Johnson JA. Pharmacogenomics: the inherited basis for interindividual differences in drug response. Annu Rev Genomics Hum Genet 2001;2:9–39.
17. Evans WE, McLeod HL. Pharmacogenomics: drug disposition, drug targets, and side effects. N Engl J Med 2003;348:538–549.
18. Weinshilboum R. Inheritance and drug response. N Engl J Med 2003;348:529–537.
19. Weinshilboum R, Wang L. Pharmacogenetics: inherited variation in amino acid sequence and altered protein quantity. Clin Pharmacol Ther 2004;75:253–258.
20. Ulrich CM, Robien K, McLeod HL. Cancer pharmacogenetics: polymorphisms, pathways and beyond. Nat Rev Cancer 2003; 3:912–920.
21. Alving AS, Carson PE, Flanagan CL, et al. Enzymatic deficiency in primaquine-sensitive erythrocytes. Science 1956;124:484–485.
22. Hughes HB, Biehl JP, Jones AP, et al. Metabolism of isoniazid in man as related to the occurrence of peripheral neuritis. Am Rev Tuberc 1954;70:266–273.
23. Evans DA, Manley KA, McKusick VA. Genetic control of isoniazid metabolism in man. Br Med J 1960;5197:485–491.
24. Kalow W. Familial incidence of low pseudocholinesterase level. Lancet 1956;ii:576.
25. Motulsky AG. Drug reactions enzymes, and biochemical genetics. J Am Med Assoc 1957;165:835–837.
26. Beutler E. The hemolytic effect of primaquine and related compounds: a review. Blood 1959;14:103–139.
27. McLeod HL, Evans WE. Pharmacogenomics: unlocking the human genome for better drug therapy. Annu Rev Pharmacol Toxicol 2001;41:101–121.
28. Roses AD. Pharmacogenetics. Hum Mol Genet 2001;10:2261–2267.
29. Gonzalez FJ, Skoda RC, Kimura S, et al. Characterization of the common genetic defect in humans deficient in debrisoquine metabolism. Nature 1988;331:442–446.
30. Ingelman-Sundberg M, Oscarson M, McLellan RA. Polymorphic human cytochrome P450 enzymes: an opportunity for individualized drug treatment. Trends Pharmacol Sci 1999;20:342–349.
31. Marshall E. Preventing toxicity with a gene test. Science 2003; 302:588–590.
32. Innocenti F, Stadler WM, Iyer L, et al. Flavopiridol metabolism in cancer patients is associated with the occurrence of diarrhea. Clin Cancer Res 2000;6:3400–3405.
33. Heggie GD, Sommadossi JP, Cross DS, et al. Clinical pharmacokinetics of 5-fluorouracil and its metabolites in plasma, urine, and bile. Cancer Res 1987;47:2203–2206.
34. Kalow W. Pharmacoanthropology and the genetics of drug metabolism. In: Kalow W, ed. Pharmacogenetics of Drug Metabolism. New York: Pergamon Press, 1992;865–877
35. Sasvari-Szekely M, Gerstner A, Ronai Z, et al. Rapid genotyping of factor V Leiden mutation using single-tube bidirectional allele-specific amplification and automated ultrathin-layer agarose gel electrophoresis. Electrophoresis 2000;21:816–821.
36. Lu Z, Diasio RB. Polymorphic drug-metabolizing enzymes. In: Schilsky RL, Milano GA, Ratain MJ, eds. Principles of Antineoplastic Drug Development and Pharmacology. New York: Dekker, 1996: 281–385.
37. Relling MV, Dervieux T. Pharmacogenetics and cancer therapy. Nat Rev Cancer 2001;1:99–108.
38. Chang TK, Yu L, Goldstein JA, et al. Identification of the polymorphically expressed CYP2C19 and the wild-type CYP2C9-ILE359 allele as low-Km catalysts of cyclophosphamide and ifosfamide activation. Pharmacogenetics 1997;7:211–221.
39. Williams ML, Bhargava P, Cherrouk I, et al. A discordance of the cytochrome P450 2C19 genotype and phenotype in patients with advanced cancer. Br J Clin Pharmacol 2000;49:485–488.
40. Boddy AV, Ratain MJ. Pharmacogenetics in cancer etiology and chemotherapy. Clin Cancer Res 1997;3:1025–1030.
41. Iyer L, Ratain MJ. Pharmacogenetics and cancer chemotherapy. Eur J Cancer 1998;34:1493–1499.
42. Krynetski EY, Evans WE. Pharmacogenetics of cancer therapy: getting personal. Am J Hum Genet 1998;63:11–16.
43. Iyer L. Inherited variations in drug-metabolizing enzymes: significance in clinical oncology. Mol Diagn 1999;4:327–333.
44. Desai AA, Innocenti F, Ratain MJ. Pharmacogenomics: road to anticancer therapeutics nirvana? Oncogene 2003;22:6621–6628.
45. Watters JW, McLeod HL. Cancer pharmacogenomics: current and future applications. Biochim Biophys Acta 2003;1603:99–111.
46. Innocenti F, Ratain MJ. Update on pharmacogenetics in cancer chemotherapy. Eur J Cancer 2002;38:639–644.
47. McLeod HL, Krynetski EY, Relling MV, et al. Genetic polymorphism of thiopurine methyltransferase and its clinical relevance for childhood acute lymphoblastic leukemia. Leukemia 2000; 14:567–572.
48. Vogt MH, Stet EH, De Abreu RA, et al. The importance of methylthio-IMP for methylmercaptopurine ribonucleoside (Me-MPR) cytotoxicity in Molt F4 human malignant T-lymphoblasts. Biochim Biophys Acta 1993;1181:189–194.
49. Lee D, Szumlanski C, Houtman J, et al. Thiopurine methyltransferase pharmacogenetics: cloning of human liver cDNA and a processed pseudogene on human chromosome 18q21.1. Drug Metab Dispos 1995;23:398–405.
50. Szumlanski C, Otterness D, Her C, et al. Thiopurine methyltransferase pharmacogenetics: human gene cloning and characterization of a common polymorphism. DNA Cell Biol 1996; 15:17–30.
51. Yates CR, Krynetski EY, Loennechen T, et al. Molecular diagnosis of thiopurine S-methyltransferase deficiency: genetic basis for azathioprine and mercaptopurine intolerance. Ann Intern Med 1997;126:608–614.
52. Otterness D, Szumlanski C, Lennard L, et al. Human thiopurine methyltransferase pharmacogenetics: gene sequence polymorphisms. Clin Pharmacol Ther 1997;62:60–73.
53. Krynetski EY, Schuetz JD, Galpin AJ, et al. A single point mutation leading to loss of catalytic activity in human thiopurine S-methyltransferase. Proc Natl Acad Sci USA 1995;92:949–953.
54. Tai HL, Krynetski EY, Yates CR, et al. Thiopurine S-methyltransferase deficiency: two nucleotide transitions define the most prevalent mutant allele associated with loss of catalytic activity in Caucasians. Am J Hum Genet 1996;58:694–702.
55. Tai HL, Krynetski EY, Schuetz EG, et al. Enhanced proteolysis of thiopurine S-methyltransferase (TPMT) encoded by mutant alleles in humans (TPMT*3A, TPMT*2): mechanisms for the genetic polymorphism of TPMT activity. Proc Natl Acad Sci USA 1997;94:6444–6449.
56. Tai HL, Fessing MY, Bonten EJ, et al. Enhanced proteasomal degradation of mutant human thiopurine S-methyltransferase (TPMT) in mammalian cells: mechanism for TPMT protein deficiency inherited by TPMT*2, TPMT*3A, TPMT*3B or TPMT*3C. Pharmacogenetics 1999;9:641–650.
57. Weinshilboum RM, Sladek SL. Mercaptopurine pharmacogenetics: monogenic inheritance of erythrocyte thiopurine methyltransferase activity. Am J Hum Genet 1980;32:651–662.
58. Ameyaw MM, Collie-Duguid ES, Powrie RH, et al. Thiopurine methyltransferase alleles in British and Ghanaian populations. Hum Mol Genet 1999;8:367–370.
59. Kubota T, Chiba K. Frequencies of thiopurine S-methyltransferase mutant alleles (TPMT*2, *3A, *3B and *3C) in 151 healthy Japanese subjects and the inheritance of TPMT*3C in the family of a propositus. Br J Clin Pharmacol 2001;51:475–477.
60. Lennard L, Lilleyman JS. Individualizing therapy with 6-mercaptopurine and 6-thioguanine related to the thiopurine methyltransferase genetic polymorphism. Ther Drug Monit 1996;18: 328–334.
61. Lennard L, Keen D, Lilleyman JS. Oral 6-mercaptopurine in childhood leukemia: parent drug pharmacokinetics and active metabolite concentrations. Clin Pharmacol Ther 1986;40:287–292.
62. Evans WE, Hon YY, Bomgaars L, et al. Preponderance of thiopurine S-methyltransferase deficiency and heterozygosity among patients intolerant to mercaptopurine or azathioprine. J Clin Oncol 2001;19:2293–2301.
63. Relling MV, Hancock ML, Boyett JM, et al. Prognostic importance of 6-mercaptopurine dose intensity in acute lymphoblastic leukemia. Blood 1999;93:2817–2823.
64. 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.
65. Milano G, Etienne MC. Fluorinated pyrimidines. In: Grochow L, Ames M, eds. A Clinician's Guide to Chemotherapy, Pharmacokinetics and Pharmacodynamics. Baltimore: Williams and Wilkins, 1998:289–300.
66. Allegra CJ, Grem JL. Antimetabolites. In: Devita VT, Rosenberg SA, eds. Cancer Principles and Practice of Oncology. Philadelphia: Lippincott-Raven, 1997:432–451.
67. Nishida M. Pharmacological and clinical properties of Xeloda (capecitabine), a new oral active derivative of fluoropyrimidine. Nippon Yakurigaku Zasshi, 2003;122:549–553.
68. Andre T, Louvet C, de Gramont A. Colon cancer: what is new in 2004? Bull Cancer 2004;91(1):75–80.
69. Argiris A, Haraf DJ, Kies MS, et al. Intensive concurrent chemoradiotherapy for head and neck cancer with 5-fluorouracil- and hydroxyurea-based regimens: reversing a pattern of failure. Oncologist 2003;8:350–360.
70. Diasio RB, Harris BE. Clinical pharmacology of 5-fluorouracil. Clin Pharmacokinet 1989;16:215–237.
71. Meropol NJ. Oral fluoropyrimidines in the treatment of colorectal cancer. Eur J Cancer 1998;34:1509–1513.
72. Daher GC, Harris BE, Diasio RB. Metabolism of pyrimidine analogues and their nucleosides. Pharmacol Ther 1990;48:189–222.
73. 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.
74. van Kuilenburg AB, Muller EW, Haasjes J, et al. Lethal outcome of a patient with a complete dihydropyrimidine dehydrogenase (DPD) deficiency after administration of 5-fluorouracil: frequency of the common IVS14+1G>A mutation causing DPD deficiency. Clin Cancer Res 2001;7:1149–1153.
75. Diasio RB. Clinical implications of dihydropyrimidine dehydrogenase on 5-FU pharmacology. Oncology (Huntingt) 2001;15 (1 Suppl 2):21–26; discussion 27.
76. Diasio RB, Beavers TL, Carpenter JT. Familial deficiency of dihydropyrimidine dehydrogenase: biochemical basis for familial pyrimidinemia and severe 5-fluorouracil-induced toxicity. J Clin Invest 1988;81:47–51.
77. Lu Z, Zhang R, Carpenter JT, et al. Decreased dihydropyrimidine dehydrogenase activity in a population of patients with breast cancer: implication for 5-fluorouracil-based chemotherapy. Clin Cancer Res 1998;4:325–329.
78. Etienne MC, Lagrange JL, Dassonville O, et al. Population study of dihydropyrimidine dehydrogenase in cancer patients. J Clin Oncol 1994;12:2248–2253.
79. Lu Z, Zhang R, Diasio RB. Dihydropyrimidine dehydrogenase activity in human peripheral blood mononuclear cells and liver: population characteristics, newly identified deficient patients, and clinical implication in 5-fluorouracil chemotherapy. Cancer Res 1993;53:5433–5438.
80. van Kuilenburg AB. Dihydropyrimidine dehydrogenase and the efficacy and toxicity of 5-fluorouracil. Eur J Cancer 2004;40: 939–950.
81. Johnson MR, Hageboutros A, Wang K, et al. Life-threatening toxicity in a dihydropyrimidine dehydrogenase-deficient patient after treatment with topical 5-fluorouracil. Clin Cancer Res 1999;5:2006–2011.
82. Johnson MR, Wang K, Diasio RB. Profound dihydropyrimidine dehydrogenase deficiency resulting from a novel compound heterozygote genotype. Clin Cancer Res 2002;8:768–774.
83. Collie-Duguid ES, Etienne MC, Milano G, et al. Known variant DPYD alleles do not explain DPD deficiency in cancer patients. Pharmacogenetics 2000;10:217–223.
84. Ezzeldin H, Johnson MR, Okamoto Y, et al. Denaturing high performance liquid chromatography analysis of the DPYD gene in patients with lethal 5-fluorouracil toxicity. Clin Cancer Res 2003;9:3021–3028.
85. Fernandez-Salguero PM, Sapone A, Wei X, et al. Lack of correlation between phenotype and genotype for the polymorphically expressed dihydropyrimidine dehydrogenase in a family of Pakistani origin. Pharmacogenetics 1997;7:161–163.
86. Vreken P, Van Kuilenburg AB, Meinsma R, et al. Dihydropyrimidine dehydrogenase (DPD) deficiency: identification and expression of missense mutations C29R, R886H and R235W. Hum Genet 1997;101:333–338.
87. Fernandez-Salguero P, Gonzalez FJ, Etienne MC, et al. Correlation between catalytic activity and protein content for the polymorphically expressed dihydropyrimidine dehydrogenase in human lymphocytes. Biochem Pharmacol 1995;50:1015–1020.
88. Johnson MR, Yan J, Shao L, et al. Semi-automated radioassay for determination of dihydropyrimidine dehydrogenase (DPD) activity: screening cancer patients for DPD deficiency, a condition associated with 5-fluorouracil toxicity. J Chromatogr B Biomed Sci Appl 1997;696:183–191.
89. Ezzeldin H, Okamoto Y, Johnson MR, et al. A high-throughput denaturing high-performance liquid chromatography method for the identification of variant alleles associated with dihydropyrimidine dehydrogenase deficiency. Anal Biochem 2002; 306:63–73.
90. Kuhara T, Ohdoi C, Ohse M, et al. Rapid gas chromatographic-mass spectrometric diagnosis of dihydropyrimidine dehydrogenase deficiency and dihydropyrimidinase deficiency. J Chromatogr B Analyt Technol Biomed Life Sci 2003;792:107–115.
91. Mattison LK, Ezzeldin H, Carpenter M, et al. Rapid identification of dihydropyrimidine dehydrogenase deficiency by using a novel 2-13C-uracil breath test. Clin Cancer Res 2004;10:2652–2658.
92. Milano G, Fischel JL, Etienne MC, et al. Inhibition of dihydropyrimidine dehydrogenase by alpha-interferon: experimental data on human tumor cell lines. Cancer Chemother Pharmacol 1994;34:147–152.
93. Yan J, Tyring SK, McCrary MM, et al. The effect of sorivudine on dihydropyrimidine dehydrogenase activity in patients with acute herpes zoster. Clin Pharmacol Ther 1997;61:563–573.
94. Maring JG, van Kuilenburg AB, Haasjes J, et al. Reduced 5-FU clearance in a patient with low DPD activity due to heterozygosity for a mutant allele of the DPYD gene. Br J Cancer 2002; 86:1028–1033.
95. Harris BE, Song R, Soong SJ, et al. Relationship between dihydropyrimidine dehydrogenase activity and plasma 5-fluorouracil levels with evidence for circadian variation of enzyme activity and plasma drug levels in cancer patients receiving 5-fluorouracil by protracted continuous infusion. Cancer Res 1990;50:197–201.
96. Leichman CG, Lenz HJ, Leichman L, et al. Quantitation of intratumoral thymidylate synthase expression predicts for disseminated colorectal cancer response and resistance to protracted-infusion fluorouracil and weekly leucovorin. J Clin Oncol 1997;15:3223–3229.
97. Salonga D, Danenberg KD, Johnson M, et al. Colorectal tumors responding to 5-fluorouracil have low gene expression levels of dihydropyrimidine dehydrogenase, thymidylate synthase, and thymidine phosphorylase. Clin Cancer Res 2000;6:1322–1327.
98. Grem JL. 5-Fluoropyrimidines. In: Chabner BA, Longo DL, eds. Cancer Chemotherapy and Biotherapy. 2nd ed. Philadelphia: WB Saunders, 1996:149–212.
99. Toxicity of fluorouracil in patients with advanced colorectal cancer: effect of administration schedule and prognostic factors. Meta-Analysis Group in Cancer. J Clin Oncol 1998;16:3537–3541.
100. Van Kuilenburg AB, Meinsma R, Zoetekouw L, et al. Increased risk of grade IV neutropenia after administration of 5-fluorouracil due to a dihydropyrimidine dehydrogenase deficiency: high prevalence of the IVS14+1g>a mutation. Int J Cancer 2002; 101:253–258.
101. Johnson MR, Diasio RB. Importance of dihydropyrimidine dehydrogenase (DPD) deficiency in patients exhibiting toxicity following treatment with 5-fluorouracil. Adv Enzyme Regul 2001;41:151–157.
102. Rothenberg ML, Kuhn JG, Burris HA 3rd, et al. Phase I and pharmacokinetic trial of weekly CPT-11. J Clin Oncol 1993;11: 2194–2204.
103. Gupta E, Lestingi TM, Mick R, et al. Metabolic fate of irinotecan in humans: correlation of glucuronidation with diarrhea. Cancer Res 1994;54:3723–3725.
104. Santos A, Zanetta S, Cresteil T, et al. Metabolism of irinotecan (CPT-11) by CYP3A4 and CYP3A5 in humans. Clin Cancer Res 2000;6:2012–2020.
105. Iyer L, Das S, Janisch L, et al. UGT1A1*28 polymorphism as a determinant of irinotecan disposition and toxicity. Pharmacogenomics J 2002;2(1):43–47.
106. Burchell B, Hume R. Molecular genetic basis of Gilbert's syndrome. J Gastroenterol Hepatol 1999;14:960–966.
107. Tukey RH, Strassburg CP. Human UDP-glucuronosyltransferases: metabolism, expression, and disease. Annu Rev Pharmacol Toxicol 2000;40:581–616.
108. Sampietro M, Iolascon A. Molecular pathology of Crigler-Najjar type I and II and Gilbert's syndromes. Haematologica 1999;84: 150–157.
109. 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.
110. Monaghan G, Ryan M, Seddon R, et al. Genetic variation in bilirubin UPD-glucuronosyltransferase gene promoter and Gilbert's syndrome. Lancet 1996;347:578–581.
111. Beutler E, Gelbart T, Demina A. Racial variability in the UDP- glucuronosyltransferase 1 (UGT1A1) promoter: a balanced polymorphism for regulation of bilirubin metabolism? Proc Natl Acad Sci USA 1998;95:8170–8174.
112. Ando Y, Saka H, Ando M, et al. Polymorphisms of UDP- glucuronosyltransferase gene and irinotecan toxicity: a pharmacogenetic analysis. Cancer Res 2000;60:6921–6926.
113. Monaghan G, Foster B, Jurima-Romet M, et al. UGT1*1 genotyping in a Canadian Inuit population. Pharmacogenetics 1997;7: 153–156.
114. Sato H, Adachi Y, Koiwai O. The genetic basis of Gilbert's syndrome. Lancet 1996;347:557–558.
115. Akaba K, Kimura T, Sasaki A, et al. Neonatal hyperbilirubinemia and mutation of the bilirubin uridine diphosphate-glucuronosyltransferase gene: a common missense mutation among Japanese, Koreans and Chinese. Biochem Mol Biol Int 1998;46: 21–26.
116. Araki E, Ishikawa M, Iigo M, et al. Relationship between development of diarrhea and the concentration of SN-38, an active metabolite of CPT-11, in the intestine and the blood plasma of athymic mice following intraperitoneal administration of CPT-11. Jpn J Cancer Res 1993;84:697–702.
117. Coles BF, Kadlubar FF. Detoxification of electrophilic compounds by glutathione S-transferase catalysis: determinants of individual response to chemical carcinogens and chemotherapeutic drugs? Biofactors 2003;17:115–130.
118. Townsend D, Tew K. Cancer drugs, genetic variation and the glutathione-S-transferase gene family. Am J Pharmacogenomics 2003;3:157–172.
119. Hayes JD, Pulford DJ. The glutathione S-transferase supergene family: regulation of GST and the contribution of the isoenzymes to cancer chemoprotection and drug resistance. Crit Rev Biochem Mol Biol 1995;30:445–600.
120. Hayes JD, Strange RC. Glutathione S-transferase polymorphisms and their biological consequences. Pharmacology 2000;61: 154–166.
121. Landi S. Mammalian class theta GST and differential susceptibility to carcinogens: a review. Mutat Res 2000;463:247–283.
122. Rebbeck TR. Molecular epidemiology of the human glutathione S-transferase genotypes GSTM1 and GSTT1 in cancer susceptibility. Cancer Epidemiol Biomarkers Prev 1997;6:733–743.
123. McLellan RA, Oscarson M, Alexandrie AK, et al. Characterization of a human glutathione S-transferase mu cluster containing a duplicated GSTM1 gene that causes ultrarapid enzyme activity. Mol Pharmacol 1997;52:958–965.
124. Nelson HH, Wiencke JK, Christiani DC, et al. Ethnic differences in the prevalence of the homozygous deleted genotype of glutathione S-transferase theta. Carcinogenesis 1995;16: 1243–1245.
125. Lee KA, Kim SH, Woo HY, et al. Increased frequencies of glutathione S-transferase (GSTM1 and GSTT1) gene deletions in Korean patients with acquired aplastic anemia. Blood 2001; 98:3483–3485.
126. Lohmueller KE, Pearce CL, Pike M, et al. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet 2003;33:177–182.
127. Howells RE, Redman CW, Dhar KK, et al. Association of glutathione S-transferase GSTM1 and GSTT1 null genotypes with clinical outcome in epithelial ovarian cancer. Clin Cancer Res 1998;4:2439–2445.
128. Ambrosone CB, Sweeney C, Coles BF, et al. Polymorphisms in glutathione S-transferases (GSTM1 and GSTT1) and survival after treatment for breast cancer. Cancer Res 2001;61:7130–7135.
129. Stanulla M, Schrappe M, Brechlin AM, et al. Polymorphisms within glutathione S-transferase genes (GSTM1, GSTT1, GSTP1) and risk of relapse in childhood B-cell precursor acute lymphoblastic leukemia: a case-control study. Blood 2000;95:1222–1228.
130. Chen CL, Liu Q, Pui CH, et al. Higher frequency of glutathione S-transferase deletions in black children with acute lymphoblastic leukemia. Blood 1997;89:1701–1707.
131. Chu E, Ju J, Schmitz J. Antifolate drugs in cancer therapy. In: Jackman A, ed. Anticancer Drug Development Guide. Totowa, NJ: Humana Press, 1999:397–408.
132. Chu E, Koeller DM, Casey JL, et al. Autoregulation of human thymidylate synthase messenger RNA translation by thymidylate synthase. Proc Natl Acad Sci USA 1991;88:8977–8981.
133. Dolnick BJ. Thymidylate synthase and the cell cycle: what should we believe? Cancer J 2000;6:215–216.
134. Marsh S, McLeod HL. Thymidylate synthase pharmacogenetics in colorectal cancer. Clin Colorectal Cancer 2001;1:175–178; discussion 179–181.
135. Pullarkat ST, Stoehlmacher J, Ghaderi V, et al. Thymidylate synthase gene polymorphism determines response and toxicity of 5-FU chemotherapy. Pharmacogenomics J 2001;1:65–70.
136. Kawakami K, Omura K, Kanehira E, et al. Polymorphic tandem repeats in the thymidylate synthase gene is associated with its protein expression in human gastrointestinal cancers. Anticancer Res 1999;19(4B):3249–3252.
137. 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.
138. Marsh S, McKay JA, Cassidy J, et al. Polymorphism in the thymidylate synthase promoter enhancer region in colorectal cancer. Int J Oncol 2001;19:383–386.
139. Mandola MV, Stoehlmacher J, Muller-Weeks S, et al. A novel single nucleotide polymorphism within the 5′ tandem repeat polymorphism of the thymidylate synthase gene abolishes USF-1 binding and alters transcriptional activity. Cancer Res 2003;63: 2898–2904.
140. 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.
141. Marsh S, Ameyaw MM, Githang'a J, et al. Novel thymidylate synthase enhancer region alleles in African populations. Hum Mutat 2000;16:528.
142. 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.
143. Lenz H.-J, Zang W, Zahedy S, et al. A 6 basepair deletion in the 3 UTR of the thymidylate synthase(TS) gene predicts TS mRNA expression in colorectal tumors: a possible candidate gene for colorectal cancer risk. Proc Am Assoc Cancer Res 2002;43:660.
144. Johnston PG, Fisher ER, Rockette HE, et al. The role of thymidylate synthase expression in prognosis and outcome of adjuvant chemotherapy in patients with rectal cancer. J Clin Oncol 1994;12:2640–2647.
145. Pestalozzi BC, McGinn CJ, Kinsella TJ, et al. Increased thymidylate synthase protein levels are principally associated with proliferation but not cell cycle phase in asynchronous human cancer cells. Br J Cancer 1995;71:1151–1157.
146. Lenz HJ, Leichman CG, Danenberg KD, et al. Thymidylate synthase mRNA level in adenocarcinoma of the stomach: a predictor for primary tumor response and overall survival. J Clin Oncol 1996;14:176–182.
147. Kornmann M, Link KH, Lenz HJ, et al. Thymidylate synthase is a predictor for response and resistance in hepatic artery infusion chemotherapy. Cancer Lett 1997;118(1):29–35.
148. Aschele C, Debernardis D, Casazza S, et al. Immunohistochemical quantitation of thymidylate synthase expression in colorectal cancer metastases predicts for clinical outcome to fluorouracil-based chemotherapy. J Clin Oncol 1999;17:1760–1770.
149. Stoehlmacher I, Mandola MV, Yun J, et al. Alterations of the thymidylate synthase (TS) pathway and colorectal cancer risk: the impact of three TS polymorphisms. Proc Am Assoc Cancer Res 2003;44:597.
150. McLeod HL, Sargent DJ, Marsh S, et al. Pharmacogenetic analysis of systemic toxicity and response after 5-fluorouracil (5FU)/CPT-11, 5FU/oxaliplatin (oral), or CPT-11/oxal therapy for advanced colorectal cancer. Proc Am Assoc Clin Oncol 2003;22:252.
151. Fisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for prevention of breast cancer: report of the National Surgical Adjuvant Breast and Bowel Project P-1 study. J Natl Cancer Inst 1998;90: 1371–1388.
152. Fabian C, Tilzer L, Sternson L. Comparative binding affinities of tamoxifen, 4-hydroxytamoxifen, and desmethyltamoxifen for estrogen receptors isolated from human breast carcinoma: correlation with blood levels in patients with metastatic breast cancer. Biopharm Drug Dispos 1981;2:381–390.
153. Johnson MD, Zuo H, Lee KH, et al. Pharmacological characterization of 4-hydroxy-N-desmethyl tamoxifen, a novel active metabolite of tamoxifen. Breast Cancer Res Treat 2004;85:151–159.
154. Stearns V, Johnson MD, Rae JM, et al. Active tamoxifen metabolite plasma concentrations after coadministration of tamoxifen and the selective serotonin reuptake inhibitor paroxetine. J Natl Cancer Inst 2003;95:1758–1764.
155. Nishiyama T, Ogura K, Nakano H, et al. Reverse geometrical selectivity in glucuronidation and sulfation of cis- and trans-4-hydroxytamoxifens by human liver UDP-glucuronosyltransferases and sulfotransferases. Biochem Pharmacol 2002;63:1817–1830.
156. Seth P, Lunetta KL, Bell DW, et al. Phenol sulfotransferases: hormonal regulation, polymorphism, and age of onset of breast cancer. Cancer Res 2000;60:6859–6863.
157. Zhu X, Veronese ME, Bernard CC, et al. Identification of two human brain aryl sulfotransferase cDNAs. Biochem Biophys Res Commun 1993;195:120–127.
158. Jones AL, Hagen M, Coughtrie MW, et al. Human platelet phenolsulfotransferases: cDNA cloning, stable expression in V79 cells and identification of a novel allelic variant of the phenol-sulfating form. Biochem Biophys Res Commun 1995;208:855–862.
159. Raftogianis RB, Wood TC, Otterness DM, et al. Phenol sulfotransferase pharmacogenetics in humans: association of common SULT1A1 alleles with TS PST phenotype. Biochem Biophys Res Commun 1997;239:298–304.
160. Coughtrie MW, Gilissen RA, Shek B, et al. Phenol sulphotransferase SULT1A1 polymorphism: molecular diagnosis and allele frequencies in Caucasian and African populations. Biochem J 1999;337(Pt 1):45–49.
161. MacCallum J, Cummings J, Dixon JM, et al. Concentrations of tamoxifen and its major metabolites in hormone responsive and resistant breast tumours. Br J Cancer 2000;82:1629–1635.
162. Zheng W, Xie D, Cerhan JR, et al. Sulfotransferase 1A1 polymorphism, endogenous estrogen exposure, well-done meat intake, and breast cancer risk. Cancer Epidemiol Biomarkers Prev 2001;10:89–94.
163. Nowell S, Sweeney C, Winters M, et al. Association between sulfotransferase 1A1 genotype and survival of breast cancer patients receiving tamoxifen therapy. J Natl Cancer Inst 2002;94: 1635–1640.
164. Henderson BE, Ross RK, Pike MC, et al. Endogenous hormones as a major factor in human cancer. Cancer Res 1982;42:3232–3239.
165. Endogenous sex hormones and breast cancer in postmenopausal women: reanalysis of nine prospective studies. J Natl Cancer Inst 2002;94:606–616.
166. Miller WR, Hawkins RA, Forrest AP. Significance of aromatase activity in human breast cancer. Cancer Res 1982;42(Suppl 8): 3365s–3368s.
167. Steinkampf MP, Mendelson CR, Simpson ER. Regulation by follicle- stimulating hormone of the synthesis of aromatase cytochrome P-450 in human granulosa cells. Mol Endocrinol 1987;1: 465–471.
168. Phornphutkul C, Okubo T, Wu K, et al. Aromatase p450 expression in a feminizing adrenal adenoma presenting as isosexual precocious puberty. J Clin Endocrinol Metab 2001;86:649–652.
169. Sumitani H, Shozu M, Segawa T, et al. In situ estrogen synthesized by aromatase P450 in uterine leiomyoma cells promotes cell growth probably via an autocrine/intracrine mechanism. Endocrinology 2000;141:3852–3861.
170. Inkster S, Yue W, Brodie A. Human testicular aromatase: immunocytochemical and biochemical studies. J Clin Endocrinol Metab 1995;80:1941–1947.
171. Esteban JM, Warsi Z, Haniu M, et al. Detection of intratumoral aromatase in breast carcinomas: an immunohistochemical study with clinicopathologic correlation. Am J Pathol 1992;140: 337–343.
172. Thompson EA Jr, Siiteri PK. Utilization of oxygen and reduced nicotinamide adenine dinucleotide phosphate by human placental microsomes during aromatization of androstenedione. J Biol Chem 1974;249:5364–5372.
173. Santen RJ, Santner S, Davis B, et al. Aminoglutethimide inhibits extraglandular estrogen production in postmenopausal women with breast carcinoma. J Clin Endocrinol Metab 1978;47: 1257–1265.
174. Karaer O, Oruc S, Koyuncu FM. Aromatase inhibitors: possible future applications. Acta Obstet Gynecol Scand 2004;83:699–706.
175. Mokbel K. The evolving role of aromatase inhibitors in breast cancer. Int J Clin Oncol 2002;7:279–283.
176. Yue W, Mor G, Naftolin F, et al. Aromatase inhibitors in breast cancer. In: Robertson JFR, Nicholson RI, Hayes DF, eds. Endocrine Therapy of Breast Cancer. London: Martin Dunitz, 2002: 75–106.
177. Santen RJ. Inhibition of aromatase: insights from recent studies. Steroids 2003;68:559–567.
178. Brodie AM, Njar VC. Aromatase inhibitors and breast cancer. Semin Oncol 1996;23(4 Suppl 9):10–20.
179. Bonneterre J, Buzdar A, Nabholtz JM, et al. Anastrozole is superior to tamoxifen as first-line therapy in hormone receptor positive advanced breast carcinoma. Cancer 2001;92:2247–2258.
180. Mouridsen H, Gershanovich M, Sun Y, et al. Superior efficacy of letrozole versus tamoxifen as first-line therapy for postmenopausal women with advanced breast cancer: results of a phase III study of the International Letrozole Breast Cancer Group. J Clin Oncol 2001;19:2596–2606.
181. Ellis MJ, Coop A, Singh B, et al. Letrozole is more effective neoadjuvant endocrine therapy than tamoxifen for ErbB-1- and/or ErbB-2-positive, estrogen receptor-positive primary breast cancer: evidence from a phase III randomized trial. J Clin Oncol 2001;19:3808–3816.
182. Gabriel SB, Schaffner SF, Nguyen H, et al. The structure of haplotype blocks in the human genome. Science 2002;296:2225–2229.
183. Haiman CA, Stram DO, Pike MC, et al. A comprehensive haplotype analysis of CYP19 and breast cancer risk: the Multiethnic Cohort. Hum Mol Genet 2003;12:2679–2692.
184. Hankinson SE, Willett WC, Manson JE, et al. Plasma sex steroid hormone levels and risk of breast cancer in postmenopausal women. J Natl Cancer Inst 1998;90:1292–1299.
185. Dunning AM, Dowsett M, Healey CS, et al. Polymorphisms associated with circulating sex hormone levels in postmenopausal women. J Natl Cancer Inst 2004;96:936–945.
186. Lloveras B, Monzo M, Colomer R, et al. Letrozole efficacy is related to human aromatase CYP19 single nucleotide polymorphisms (SNPs) in metastatic breast cancer patients. Journal of Clinical Oncology 2004;22:507.
187. Goetz MP, Ames MM, Weinshilboum RM. Primer on medical genomics, Part XII: pharmacogenomics: general principles with cancer as a model. Mayo Clin Proc 2004;79:376–384.
188. Jamieson ER, Lippard SJ. Structure, recognition, and processing of cisplatin-DNA adducts. Chem Rev 1999;99:2467–2498.
189. Furuta T, Ueda T, Aune G, et al. Transcription-coupled nucleotide excision repair as a determinant of cisplatin sensitivity of human cells. Cancer Res 2002;62:4899–4902.
190. Dabholkar M, Vionnet J, Bostick-Bruton F, et al. Messenger RNA levels of XPAC and ERCC1 in ovarian cancer tissue correlate with response to platinum-based chemotherapy. J Clin Invest 1994; 94:703–708.
191. Li Q, Yu JJ, Mu C, et al. Association between the level of ERCC-1 expression and the repair of cisplatin-induced DNA damage in human ovarian cancer cells. Anticancer Res 2000;20(2A):645–652.
192. Metzger R, Leichman CG, Danenberg KD, et al. ERCC1 mRNA levels complement thymidylate synthase mRNA levels in predicting response and survival for gastric cancer patients receiving combination cisplatin and fluorouracil chemotherapy. J Clin Oncol 1998;16:309–316.
193. Park DJ, Stoehlmacher J, Zhang W, et al. A xeroderma pigmentosum group D gene polymorphism predicts clinical outcome to platinum-based chemotherapy in patients with advanced colorectal cancer. Cancer Res 2001;61:8654–8658.
194. Shen MR, Jones IM, Mohrenweiser H. Nonconservative amino acid substitution variants exist at polymorphic frequency in DNA repair genes in healthy humans. Cancer Res 1998;58: 604–608.
195. Yu JJ, Mu C, Lee KB, et al. A nucleotide polymorphism in ERCC1 in human ovarian cancer cell lines and tumor tissues. Mutat Res 1997;382:13–20.
196. Zhou W, Gurubhagavatula S, Liu G, et al. Excision repair cross-complementation group 1 polymorphism predicts overall survival in advanced non-small cell lung cancer patients treated with platinum-based chemotherapy. Clin Cancer Res 2004;10: 4939–4943.
197. Lunn RM, Helzlsouer KJ, Parshad R, et al. XPD polymorphisms: effects on DNA repair proficiency. Carcinogenesis 2000;21: 551–555.
198. Spitz MR, Wu X, Wang Y, et al. Modulation of nucleotide excision repair capacity by XPD polymorphisms in lung cancer patients. Cancer Res 2001;61:1354–1357.
199. Qiao Y, Spitz MR, Shen H, et al. Modulation of repair of ultraviolet damage in the host-cell reactivation assay by polymorphic XPC and XPD/ERCC2 genotypes. Carcinogenesis 2002;23:295–299.
200. Lunn RM, Langlois RG, Hsieh LL, et al. XRCC1 polymorphisms: effects on aflatoxin B1-DNA adducts and glycophorin A variant frequency. Cancer Res 1999;59:2557–2561.
201. Stoehlmacher J, Ghaderi V, Iobal S, et al. A polymorphism of the XRCC1 gene predicts for response to platinum based treatment in advanced colorectal cancer. Anticancer Res 2001;21(4B): 3075–3079.
202. El-Rayes BF, LoRusso PM. Targeting the epidermal growth factor receptor. Br J Cancer 2004;91:418–424.
203. Green MR. Targeting targeted therapy. N Engl J Med 2004; 350:2191–2193.
204. Mendelsohn J, Baselga J. Status of epidermal growth factor receptor antagonists in the biology and treatment of cancer. J Clin Oncol 2003;21:2787–2799.
205. Kris MG, Natale RB, Herbst RS, et al. Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial. JAMA 2003;290:2149–2158.
206. Fukuoka M, Yano S, Giaccone G, et al. Multi-institutional randomized phase II trial of gefitinib for previously treated patients with advanced non-small-cell lung cancer. J Clin Oncol 2003; 21:2237–2246.
207. Cohen MH, Williams GA, Sridhara R, et al. United States Food and Drug Administration Drug Approval summary: Gefitinib (ZD1839; Iressa) tablets. Clin Cancer Res 2004;10:1212–1218.
208. 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.
209. Sparreboom A, Danesi R, Ando Y, et al. Pharmacogenomics of ABC transporters and its role in cancer chemotherapy. Drug Resist Updat 2003;6:71–84.
210. Toffoli G, Veronesi A, Boiocchi M, et al. MTHFR gene polymorphism and severe toxicity during adjuvant treatment of early breast cancer with cyclophosphamide, methotrexate, and fluorouracil (CMF). Ann Oncol 2000;11:373–374.
211. Kalow W. Pharmacogenetics: its biologic roots and the medical challenge. Clin Pharmacol Ther 1993;54:235–241.
212. Meyers U, Skoda RC, Zanger UM, et al. The genetic polymorphism of debrisoquine/sparteine metabolism: molecular mechanisms. In: Kalow W, ed. Pharmacogenetics of Drug Metabolism. New York: Pergamon Press, 1992;609–623.