In the past decade, significant strides have been made in the area of cardiovascular pharmacogenomic research, with the discovery of associations between certain genotypes and drug-response phenotypes. While the motivations for personalized and predictive medicine are promising for patient care and support a model of health system efficiency, the implementation of pharmacogenomics for cardiovascular therapeutics on a population scale faces substantial challenges. The greatest obstacle to clinical implementation of cardiovascular pharmacogenetics may be the lack of both reproducibility and agreement about the validity and utility of the findings. In this review, we present the scientific evidence in the literature for diagnostic variants for the US FDA-labeled cardiovascular therapies, namely CYP2C19 and clopidogrel, CYP2C9/VKORC1 and warfarin, and CYP2D6/ADRB1 and β-blockers. We also discuss the effect of HMGCR/LDLR in decreasing the effectiveness of low-density lipoprotein cholesterol with statin therapy, the SLCO1B1 genotype and simvastatin myotoxicity, and ADRB1/ADD1 for antihypertensive response.
biomarker; cardiovascular; clinical utility; clopidogrel; drug label; genetics; personalized medicine; pharmacogenetics; predictive medicine; warfarin
To extend to biomarker studies the consensus clinical significance criterion of a three-point difference in Hamilton Rating Scale for Depression.
Materials & methods
We simulated datasets modeled on large clinical trials.
In a typical clinical trial comparing active treatment and placebo, a difference of three Hamilton Rating Scale for Depression (HRSD) points at the end of treatment corresponds to 6.3% of variance in outcome explained. To achieve a similar explanatory power, genotypes with minor allele frequencies of 5, 10, 20, 30 and 50% need to attain a per allele difference of 4.7, 3.6, 2.8, 2.4 and 2.2 HRSD points, respectively. A normally distributed continuous biomarker will need an effect size of 1.5 HRSD points per standard deviation. A number needed to assess of three suggests that with this effect size, a biomarker will significantly improve the prediction of outcome in one out of every three patients assessed.
This report provides guidance on assessing clinical significance of biomarkers predictive of outcome in depression treatment.
antidepressant medication; biomarkers; clinical significance; major depressive disorder; number needed to assess
Hepatic enzymes, CYP2B6 and UGT2B7 play a major role in the metabolism of the widely used antiretroviral drugs efavirenz, nevirapine and zidovudine. In the present study, we provide a view of UGT2B7 haplotype structure, and quantify the genetic diversity and differentiation at both CYP2B6 and UGT2B7 genes on a worldwide scale.
Materials & methods
We genotyped one intronic and three promoter SNPs, and together with three nonsynonymous SNPs, inferred UGT2B7 alleles in north American (n = 326), west African (n = 133) and Papua New Guinean (n = 142) populations. We also included genotype data for five CYP2B6 and six UGT2B7 SNPs from an additional 12 worldwide populations (n = 629) analyzed in the 1000 Genomes Project.
We observed significant differences in certain SNP and allele frequencies of CYP2B6 and UGT2B7 among worldwide populations. Diversity values were higher for UGT2B7 than for CYP2B6, although there was more diversity between populations for CYP2B6. For both genes, most of the genetic variation was observed among individuals within populations, with the Papua New Guinean population showing the highest pairwise differentiation values for CYP2B6, and the Asian and European populations showing higher pairwise differentiation values for UGT2B7.
These new genetic distinctions provide additional insights for investigating differences in antiretroviral pharmacokinetics and therapy outcomes among ethnically and geographically diverse populations.
1000 Genomes Project; CYP2B6; efavirenz; HIV/AIDS; nevirapine; Papua New Guinea; UGT2B7; zidovudine
This study was aimed at developing a pharmacogenetic-driven warfarin-dosing algorithm in 163 admixed Puerto Rican patients on stable warfarin therapy.
Patients & methods
A multiple linear-regression analysis was performed using log-transformed effective warfarin dose as the dependent variable, and combining CYP2C9 and VKORC1 genotyping with other relevant nongenetic clinical and demographic factors as independent predictors.
The model explained more than two-thirds of the observed variance in the warfarin dose among Puerto Ricans, and also produced significantly better ‘ideal dose’ estimates than two pharmacogenetic models and clinical algorithms published previously, with the greatest benefit seen in patients ultimately requiring <7 mg/day. We also assessed the clinical validity of the model using an independent validation cohort of 55 Puerto Rican patients from Hartford, CT, USA (R2 = 51%).
Our findings provide the basis for planning prospective pharmacogenetic studies to demonstrate the clinical utility of genotyping warfarin-treated Puerto Rican patients.
algorithm; CYP2C9; genotyping; personalized medicine; pharmacogenetics; VKORC1; warfarin
Warfarin pharmacogenomic algorithms reduce dosing error, but perform poorly in non-European–Americans. Electronic health record (EHR) systems linked to biobanks may allow for pharmacogenomic analysis, but they have not yet been used for this purpose.
Patients & methods
We used BioVU, the Vanderbilt EHR-linked DNA repository, to identify European–Americans (n = 1022) and African–Americans (n = 145) on stable warfarin therapy and evaluated the effect of 15 pharmacogenetic variants on stable warfarin dose.
Associations between variants in VKORC1, CYP2C9 and CYP4F2 with weekly dose were observed in European–Americans as well as additional variants in CYP2C9 and CALU in African–Americans. Compared with traditional 5 mg/day dosing, implementing the US FDA recommendations or the International Warfarin Pharmacogenomics Consortium (IWPC) algorithm reduced error in weekly dose in European–Americans (13.5–12.4 and 9.5 mg/week, respectively) but less so in African–Americans (15.2–15.0 and 13.8 mg/week, respectively). By further incorporating associated variants specific for European–Americans and African–Americans in an expanded algorithm, dose-prediction error reduced to 9.1 mg/week (95% CI: 8.4–9.6) in European–Americans and 12.4 mg/week (95% CI: 10.0–13.2) in African–Americans. The expanded algorithm explained 41 and 53% of dose variation in African–Americans and European–Americans, respectively, compared with 29 and 50%, respectively, for the IWPC algorithm. Implementing these predictions via dispensable pill regimens similarly reduced dosing error.
These results validate EHR-linked DNA biorepositories as real-world resources for pharmacogenomic validation and discovery.
anticoagulants; bioinformatics; electronic health record; genes; pharmacogenomics; warfarin
B-RAF; melanoma; N-RAS; targeted therapy; vemurafenib; whole-exome sequencing
cardiovascular; generic drug products; genotyping; neuropsychiatric; personalized medicine; pharmacogenomics
To develop and apply a novel genotyping method for the 9-bp exon 1 insertion/deletion polymorphism in BDKRB2.
Materials & methods
DNA from 718 patients with heart failure was extracted using standard methods and a region containing exon 1 of BDKRB2 was amplified with PCR. The PCR product was separated using the Qiagen QIAxcel® capillary electrophoresis system. The bp size of the PCR product was calculated and the genotypes determined using Qiagen BioCalculator® software.
Capillary electrophoresis accurately genotyped samples with >99% call rate and 700 s run time per row of a 96-well plate (i.e., less than 1 min per sample). The frequency of the deletion was 49% in the Caucasian patients (n = 441) and 45% in the African–American (n = 277).
Capillary electrophoresis is a rapid, accurate and sensitive method for genotyping the 9-bp exon 1 insertion/deletion polymorphism in BDKRB2.
9-bp exon 1 insertion/deletion; BDKRB2; bradykinin; capillary gel electrophoresis; heart failure; polymorphism; QIAxcel®
To determine if copy number variants contribute to warfarin dose requirements, we investigated CYP2C9, VKORC1, CYP4F2, GGCX and CALU for deletions and duplications in a multiethnic patient population treated with therapeutic doses of warfarin.
Patients & methods
DNA samples from 178 patients were subjected to copy number analyses by multiplex ligation-dependent probe amplification or quantitative PCR assays. Additionally, the CYP2C9 exon 8 insertion/deletion polymorphism (rs71668942) was examined among the patient cohort and 1750 additional multiethnic healthy individuals.
All patients carried two copies of CYP2C9 by multiplex ligation-dependent probe amplification and no exon 8 deletion carriers were detected. Similarly, quantitative PCR assays for VKORC1, CYP4F2, GGCX and CALU identified two copies in all populations.
These data indicate that copy number variants in the principal genes involved in warfarin dose variability (CYP2C9, VKORC1), including genes with lesser effect (CYP4F2, GGCX), and those that may be more relevant among certain racial groups (CALU), are rare in multiethnic populations, including African–Americans.
CALU; CNV; copy number variation; CYP2C9; CYP4F2; GGCX; pharmacogenetics; VKORC1; warfarin
Expression quantitative trait locus (eQTL) analysis is rapidly moving from a cutting-edge concept in genomics to a mature area of investigation, with important connections to genome-wide association studies for human disease, pharmacogenomics and toxicogenomics. Despite the importance of the topic, many investigators must develop their own code or use tools not specifically suited for eQTL analysis. Convenient computational tools are becoming available, but they are not widely publicized, and investigators who are interested in discovery or eQTL, or in using them to interpret genome-wide association study results may have difficulty navigating the available resources. The purpose of this review is to help investigators find appropriate programs for eQTL analysis and interpretation.
bioinformatics; fast linear modeling; gene expression
Humans exhibit genetic polymorphism in NAT2 resulting in rapid, intermediate and slow acetylator phenotypes. Over 65 NAT2 variants possessing one or more SNPs in the 870-bp NAT2 coding region have been reported. The seven most frequent SNPs are rs1801279 (191G>A), rs1041983 (282C>T), rs1801280 (341T>C), rs1799929 (481C>T), rs1799930 (590G>A), rs1208 (803A>G) and rs1799931 (857G>A). The majority of studies investigate the NAT2 genotype assay for three SNPs: 481C>T, 590G>A and 857G>A. A tag-SNP (rs1495741) recently identified in a genome-wide association study has also been proposed as a biomarker for the NAT2 phenotype.
Materials & methods
Sulfamethazine N-acetyltransferase catalytic activities were measured in cryopreserved human hepatocytes from a convenience sample of individuals in the USA with an ethnic frequency similar to the 2010 US population census. These activities were segregated by the tag-SNP rs1495741 and each of the seven SNPs described above. We assessed the accuracy of the tag-SNP and various two-, three-, four- and seven-SNP genotyping panels for their ability to accurately infer NAT2 phenotype.
The accuracy of the various NAT2 SNP genotype panels to infer NAT2 phenotype were as follows: seven-SNP: 98.4%; tag-SNP: 77.7%; two-SNP: 96.1%; three-SNP: 92.2%; and four-SNP: 98.4%.
A NAT2 four-SNP genotype panel of rs1801279 (191G>A), rs1801280 (341T>C), rs1799930 (590G>A) and rs1799931 (857G>A) infers NAT2 acetylator phenotype with high accuracy, and is recommended over the tag-, two-, three- and (for economy of scale) the seven-SNP genotyping panels, particularly in populations of non-European ancestry.
acetylator genotype; acetylator phenotype; cryopreserved; human hepatocyte; NAT2; SNP
Genetic polymorphisms have the potential to influence drug metabolism and vary among ethnic groups. This study evaluated the correlation of genetic polymorphisms with nevirapine pharmacokinetics exposure in Malawians.
Materials & methods
CYP450 2B6, 2D6, 3A4 and 3A5, ABCB1 and constitutive androstane receptor and pregnane X receptor, were analyzed for polymorphisms in 26 subjects.
Allele frequencies (variant) were: CYP2B6 514G>T (0.31) CYP2D6*4 (0.02); CYP2D6*17 (0.35); CYP3A4*1B (0.77); CYP3A5*3 (0.25); ABCB1 2677G>T (0.0), ABCB1 3435C>T (0.21), NR1I3 13711152T>C (0.02), NR1I2 44477T>C (0.10), NR1I2 63396C>T (0.33), NR1I2 6-bp indel (del: 0.17). CYP2B6 516G>T (non-wild-type/wild-type) correlated with nevirapine pharmacokinetic parameters; geometric mean ratios (95% CI): 1.75 (1.27–2.40) for area under the concentration time curve (AUC)0–12 h, 1.58 (1.03–2.42) for C0, and 0.53 (0.31–0.91) for clearance. In a multivariable model, nevirapine AUC increased by 1.5% per year of age (p < 0.0001), CYP2B6 516 T allele increased AUC by 92% (p < 0.0001), and CYP3A5*3 decreased AUC by 31% (p = 0.0027).
Allele frequencies were similar to other sub-Saharan African populations. The T allele for CYP2B6 516 was significantly associated with nevirapine exposure.
CYP2B6; CYP450; Malawi; nevirapine; nuclear receptor; P-glycoprotein; pharmacokinetics
The ability to predict how an individual patient will respond to a particular treatment is the ambitious goal of personalized medicine. The genetic make up of an individual has been shown to play a role in drug response. For pharmacogenomic studies, human lymphoblastoid cell lines (LCLs) comprise a useful model system for identifying genetic variants associated with pharmacologic phenotypes. The availability of extensive genotype data for many panels of LCLs derived from individuals of diverse ancestry allows for the study of genetic variants contributing to interethnic and interindividual variation in susceptibility to drugs. Many genome-wide association studies for drug-induced phenotypes have been performed in LCLs, often incorporating gene-expression data. LCLs are also being used in follow-up studies to clinical findings to determine how an associated variant functions to affect phenotype. This review describes the most recent pharmacogenomic findings made in LCLs, including the translation of some findings to clinical cohorts.
β-blockers; acetaminophen; chemotherapy; cytotoxicity; gene expression; genome-wide association studies; HapMap; immunosuppressants; lymphoblastoid cell lines; pharmacogenomics; radiation; selective serotonin reuptake inhibitors; statins
The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data – transcriptomic, proteomic and methylomic to name a few. Analyzing this ‘meta-dimensional’ data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.
computational methods; data integration; pharmacogenomics; systems biology
Pharmacogenetics aims to elucidate the genetic factors underlying the individual’s response to pharmacotherapy. Coupled with the recent (and ongoing) progress in high-throughput genotyping, sequencing and other genomic technologies, pharmacogenetics is rapidly transforming into pharmacogenomics, while pursuing the primary goals of identifying and studying the genetic contribution to drug therapy response and adverse effects, and existing drug characterization and new drug discovery. Accomplishment of both of these goals hinges on gaining a better understanding of the underlying biological systems; however, reverse-engineering biological system models from the massive datasets generated by the large-scale genetic epidemiology studies presents a formidable data analysis challenge. In this article, we review the recent progress made in developing such data analysis methodology within the paradigm of systems biology research that broadly aims to gain a ‘holistic’, or ‘mechanistic’ understanding of biological systems by attempting to capture the entirety of interactions between the components (genetic and otherwise) of the system.
biological networks; data analysis methodology; genome-wide association studies; metabolomics; pharmacogenomics; systems biology
diagnosis; disease; drug resistance; epidemiology; genetic variation; genomic medicine; individualized medicine; microRNA; miRNA; miRSNP; mutations; ncRNA; noncoding RNA; personalized medicine; pharmacogenomics; polymorphisms; prognosis
Tamoxifen biotransformation to endoxifen, a potent antiestrogen, is catalyzed by CYP2D6. In addition, CYP2C19 and SULT1A1 have also been implicated in the metabolism of tamoxifen. We sought to evaluate the importance of SULT1A1 copy number and CYP2C19*17 on disease-free survival (DFS) in postmenopausal women randomized to tamoxifen monotherapy in North Central Cancer Treatment Group 89-30-52 from January 1991 to April 1995.
Materials & methods
We extracted DNA from paraffin-embedded tumors and determined tumor SULT1A1 copy number and CYP2C19*17 genotype. The association of genotype with DFS was determined using the log-rank test. Multivariate cox modeling was performed using traditional prognostic factors, as well as CYP2D6 genotype. SULT1A1 copy number and CYP2C19*17 genotype was determined in 190 out of 256 patients (95% Caucasian).
The median follow-up for living patients was 14 years. DFS did not differ according to SULT1A1 copy number (p = 0.482) or CYP2C19*17 genotype (p = 0.667). Neither SULT1A1 copy number or CYP2C19*17 genotype was associated with disease recurrence in this cohort.
Future studies are needed to identify whether other genetic and environmental factors which affect tamoxifen metabolism are associated with tamoxifen clinical outcomes.
breast cancer; copy number polymorphism; CYP2C19; pharmacogenomic; polymorphism; single nucleotide; SULT1A1; tamoxifen
An association between carbamazepine-induced hypersensitivity and HLA-A*3101 has been reported in populations of both European and Asian descent. We aimed to investigate HLA-A*3101 and other common variants across the genome as markers for cutaneous adverse drug reactions (cADRs) attributed to lamotrigine and phenytoin.
Materials & methods
We recruited patients with lamotrigine-induced cADRs (n = 46) and patients with phenytoin-cADRs (n = 44) and the 1958 British birth cohort was used as a control (n = 1296). HLA-A*3101 was imputed from genome-wide association study data. We applied genome-wide association to study lamotrigine- and phenytoin-induced cADR, and total cADR cases combined.
Neither HLA-A*3101 nor any other genetic marker significantly predicted lamotrigine- or phenytoin-induced cADRs.
HLA-A*3101 does not appear to be a predictor for lamotrigine- and phenytoin-induced cADRs in Europeans. Our genome-wide association study results do not support the existence of a clinically relevant common variant for the development of lamotrigine- or phenytoin-induced cADRs. As a predictive marker, HLA-A*3101 appears to be specific for carbamazepine-induced cADRs.
epilepsy; GWAS; HLA-A*3101; hypersensitivity; lamotrigine; phenytoin
The heart diseases that account for a large amount of morbidity and mortality in the developed world (coronary artery disease, myocardial infarction and heart failure) are phenotypically heterogeneous disorders. It has been suspected for many years that genetics may have an important role in these diseases and their poor outcome. However, their complex and likely polygenic pathophysiology has confounded clear understanding of the genetic contribution to their etiology. Despite technological progress and great promise associated with genome-wide association studies, to date the results of their application to coronary artery disease, myocardial infarction and heart failure have yielded limited insights into these common diseases. This review discusses the current status of genome-wide association studies as they have been applied to these cohorts. The potential limitations of these studies, as well as potential future directions for identifying important genes are also discussed.
coronary artery disease; genome-wide association study; genomics; heart failure
Individualization of cancer chemotherapy based on the patient’s genetic makeup holds promise for reducing side effects and improving efficacy. However, the relative contribution of genetics to drug response is unknown.
Materials & methods
In this study, we investigated the cytotoxic effect of 29 commonly prescribed chemotherapeutic agents from diverse drug classes on 125 lymphoblastoid cell lines derived from 14 extended families.
The results of this systematic study highlight the variable role that genetics plays in response to cytotoxic drugs, ranging from a heritability of <0.15 for gemcitabine to >0.60 for epirubicin.
Putative quantitative trait loci for cytotoxic response were identified, as well as drug class-specific signatures, which could indicate possible shared genetic mechanisms. In addition to the identification of putative quantitative trait locis, the results of this study inform the prioritization of chemotherapeutic drugs with a sizable genetic response component for future investigation.
cancer; cell line; chemotherapy; cytotoxicity; pharmacogenetics; pharmacogenomic; QTL
Cisplatin ototoxicity affects different individuals in a widely variable manner. These variations are likely to be explained by genetic differences among those affected. It would be highly advantageous to identify genetic variants that predispose to cisplatin ototoxicity in order to minimize the risk to susceptible subgroups. Although this area of research is very important, only a few studies have rigorously examined the genetic basis for cisplatin-induced susceptibility to hearing loss. This article addresses recent progress in clarifying the incidence of cisplatin ototoxicity and the risk factors and controversies regarding the identifcation of genetic variants associated with cisplatin-induced hearing loss.
audiometry; cisplatin; COMT; genome-wide screening; glutathione; GST; GSTM1; GSTP1; megalin; ototoxicity; pharmacogenomics; SNP; TMPT; XPC
The information gained from pharmacogenomic testing is becoming increasingly recognized as an opportunity to improve our current dosing strategies for children. The identification of gene polymorphisms that influence drug disposition and effect can be used to help predict a child’s susceptibility to toxicity and/or response to a particular drug or therapeutic regimen. However, the potential consequences of performing genomic ana lysis in children raise important ethical considerations. Although the level of risk introduced remains partially hypothetical, awareness of the ethical concerns and protective legislation will be an important part of fully informing patients, families, clinicians, and researchers about the risks and benefits of pharmacogenomic testing in children. Where it can be done without loss of benefit, risk reduction is a moral imperative, and so the ethical complexities related to pharmacogenomics must be addressed in an ongoing way as we continue to learn more about the value of the technology to children.
child; DNA; pharmacogenetic; pharmacokinetic; SNPs
Drug therapy can be ineffective or cause adverse reactions in a subset of patients. Pharmacogenomic biomarkers afford the opportunity to optimize an individual’s therapy. Yet, few tests are currently part of standard care. To validate biomarkers, clinical replication studies are essential. Equally important, but less appreciated, the genetic mechanisms must also be understood to facilitate translation into clinical use. Representing main contributors to genetic variability, regulatory polymorphisms in particular are still poorly studied (e.g., 5-HTTLPR). This article focuses on molecular and functional diversity of genetic biomarkers, as a guide to optimal use in personalized medicine.
biomarkers; gene expression; pharmacogenomics; serotonin transporter; structural RNA polymorphisms
This study evaluated the impact of SULT4A1 gene variation on psychopathology and antipsychotic drug response in Caucasian subjects from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study and a replication sample.
Patients & methods
SULT4A1 haplotypes were determined using SNP data. The relationship to baseline psychopathology was evaluated using linear regression of Positive and Negative Syndrome Scale (PANSS) total score. Drug response was evaluated using Mixed Model Repeat Measures (MMRM) for change in PANSS.
For the CATIE sample, patients carrying a haplotype designated SULT4A1-1(+) displayed higher baseline PANSS (p = 0.03) and, when treated with olanzapine, demonstrated a significant interaction with time (p = 0.009) in the MMRM. SULT4A1-1(+) patients treated with olanzapine displayed improved response compared with SULT4A1-1(−) patients treated with olanzapine (p = 0.008) or to SULT4A1-1(+) patients treated with risperidone (p = 0.006). In the replication sample, SULT4A1-1(+) patients treated with olanzapine demonstrated greater improvement than SULT4A1-1(−) patients treated with olanzapine (p = 0.05) or than SULT4A1-1(+) patients treated with risperidone (p = 0.05).
If validated, determination of SULT4A1-1 haplotype status might be useful for identifying patients who show an enhanced response to long-term olanzapine treatment.
CATIE; olanzapine; pharmacogenomics; risperidone