The limits of genome-wide methods for pharmacogenomic testing

Pharmacogenomic landscape of the Thai population from genome sequencing of 949 individuals

Inter-individual variability in drug responses is significantly influenced by genetic factors, underscoring the importance of population-specific pharmacogenomic studies to optimize clinical outcomes. In this study, we analyzed whole genome sequencing data from 949 unrelated Thai individuals and conducted an in-depth analysis of 3239 genes involved in drug pharmacokinetics, pharmacodynamics, or immune-mediated adverse drug reactions. We identified 43 single nucleotide polymorphisms (SNPs), 134 diplotypes, and 15 human leukocyte antigen (HLA) alleles, all with moderate to high clinical significance. On average, each Thai individual carried 14 SNPs, one to two HLA alleles, and six diplotypes with actionable phenotypic associations. Clinically important diplotypes were present in over 20% of individuals for seven genes (CYP2A6, CYP2B6, CYP2C19, CYP3A5, NAT2, SLCO1B1, and VKORC1). In addition, clinically significant SNPs with allele frequencies exceeding 20% were identified among 15 genes, including VKORC1, CYP4F2, and ABCG2. We also identified 21,211 potentially deleterious variants among 3239 genes. Of these variants, 3746 were novel. The comprehensive dataset from this study serves as a valuable resource of pharmacogenomic variants in the Thai population, which will facilitate the development of personalized drug therapies and enhance patient care in Thailand.

Co-Occurring Methylenetetrahydrofolate Reductase (MTHFR) rs1801133 and rs1801131 Genotypes as Associative Genetic Modifiers of Clinical Severity in Rett Syndrome

AIM

Remethylation disorders such as 5,10-methylenetetrahydrofolate reductase (MTHFR) deficiency reduce the remethylation of homocysteine to methionine. The resulting hyperhomocysteinemia can lead to serious neurological consequences and multisystem toxicity. The role of MTHFR genotypes has not been investigated in patients with Rett Syndrome (RTT). In this study, we sought to assess the impact of co-occurring MTHFR genotypes on symptom profiles in RTT.

METHOD

Using pharmacogenomic (PGx) testing, the MTHFR genetic polymorphisms rs1801133 (c.665C>T mutation) and rs1801131 (c.1286A>C mutation) were determined in 65 patients (18.7 years ± 12.1 [mean ± standard deviation]) with RTT as part of routine clinical care within the Centre for Interventional Paediatric Psychopharmacology (CIPP) Rett Centre, a National and Specialist Child and Adolescent Mental Health Service (CAMHS) in the UK. The clinical severity of patients was assessed using the RTT-anchored Clinical Global Impression Scale (RTT-CGI).

RESULTS

The clinical severity symptom distribution varied between the homozygous and heterozygous MTHFR rs1801133 and rs1801131 genotypes. Those with the homozygous genotype had a narrower spread of severity scores across several domains (language and communication, ambulation, hand-use and eye contact clinical domains). Patients with the homozygous genotype had statistically significantly greater CGI-Severity scores than individuals with a non-homozygous MTHFR genotype (Z = -2.44, p = 0.015). When comparing the ratings of moderately impaired (4), markedly impaired (5), severely impaired (6) and extremely impaired (7), individuals with the homozygous MTHFR genotype were more impaired than those with the non-homozygous MTHFR genotype (Z = -2.06, p = 0.039). There was no statistically significant difference in the number of prescribed anti-epileptic drugs between the genotypes.

CONCLUSIONS

Our findings show that in those with a pathogenic RTT genetic variant, co-occurring homozygotic MTHFR rs1801133 and rs1801131 polymorphisms may act as associative genetic modifiers of clinical severity in a subset of patients. Profiling of rs1801133 and rs1801131 in RTT may therefore be useful, especially for high-risk patients who may be at the most risk from symptom deterioration.

Pharmacogenomic allele coverage of genome-wide genotyping arrays: a comparative analysis

The use of genome-wide genotyping arrays in pharmacogenomics (PGx) research and clinical implementation applications is increasing but it is unclear which arrays are best suited for these applications. Here, we conduct a comparative coverage analysis of PGx alleles included on genome-wide genotyping arrays, with an emphasis on alleles in genes with PGx-based prescribing guidelines. Genomic manifest files for seven arrays including the Axiom Precision Medicine Diversity Array (PMDA), Axiom PMDA Plus, Axiom PangenomiX, Axiom PangenomiX Plus, Infinium Global Screening Array, Infinium Global Diversity Array (GDA) and Infinium GDA with enhanced PGx (GDA-PGx) Array, were evaluated for coverage of 523 star alleles across 19 pharmacogenes included in prescribing guidelines developed by the Clinical Pharmacogenetic Implementation Consortium and Dutch Pharmacogenomics Working Group. Specific attention was given to coverage of the Association of Molecular Pathology's Tier 1 and Tier 2 allele sets for CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, NUDT15, TPMT and VKORC1 . Coverage of the examined PGx alleles was highest for the Infinium GDA-PGx (88%), Axiom PangenomiX Plus (77%), Axiom PangenomiX (72%) and Axiom PMDA Plus (70%). Three arrays (Infinium GDA-PGx, Axiom PangenomiX Plus and Axiom PMDA Plus) fully covered the Tier 1 alleles and the Axiom PangenomiX array provided full coverage of Tier 2 alleles. In conclusion, PGx allele coverage varied by gene and array. A superior array for all PGx applications was not identified. Future comparative analyses of genotype data produced by these arrays are needed to determine the robustness of the reported coverage estimates.

Metabolomics: A Tool to Envisage Biomarkers in Clinical Interpretation of Cancer

BACKGROUND

Cancer is amongst the most dreadful ailments of modern times, and its impact continuously worsens global health systems. Early diagnosis and suitable therapeutic agents are the prime keys to managing this disease. Metabolomics deals with the complete profiling of cells and physiological phenomena in their organelles, thus helping in keen knowledge of the pathological status of the disease. It has been proven to be one of the best strategies in the early screening of cancer.

OBJECTIVE

This review has covered the recent updates on the promising role of metabolomics in the identification of significant biochemical markers in cancer-prone individuals that could lead to the identification of cancer in the early stages.

METHODS

The literature was collected through various databases, like Scopus, PubMed, and Google Scholar, with stress laid on the last ten years' publications.

CONCLUSION

It was assessed in this review that early recognition of cancerous growth could be achieved via complete metabolic profiling in association with transcriptomics and proteomics. The outcomes are rooted in various clinical studies that anticipated various biomarkers like tryptophan, phenylalanine, lactates, and different metabolic pathways associated with the Warburg effect. This metabolite imaging has been a fundamental step for the target acquisition, evaluation of predictive cancer biomarkers for early detection, and outlooks into cancer therapy along with critical evaluation. Significant efforts should be made to make this technique most reliable and easy.

PharmVIP: A Web-Based Tool for Pharmacogenomic Variant Analysis and Interpretation

The increasing availability of next generation sequencing (NGS) for personal genomics could promote pharmacogenomics (PGx) discovery and application. However, current tools for analysis and interpretation of pharmacogenomic variants from NGS data are inadequate, as none offer comprehensive analytic functions in a simple, web-based platform. In addition, no tools exist to analyze human leukocyte antigen (HLA) genes for determining potential risks of immune-mediated adverse drug reaction (IM-ADR). We describe PharmVIP, a web-based PGx tool, for one-stop comprehensive analysis and interpretation of genome-wide variants obtained from NGS platforms. PharmVIP comprises three main interpretation modules covering analyses of pharmacogenes involved in pharmacokinetics, pharmacodynamics and IM-ADR. The Guideline module provides Clinical Pharmacogenetics Implementation Consortium (CPIC) drug guideline recommendations based on the translation of genotypic data in genes having guidelines. The HLA module reports HLA genotypes, potential adverse drug reactions, and the relevant drug guidelines. The Pharmacogenes module is employed for prioritizing variants according to variant effect on gene function. Detailed, customizable reports are provided as exportable files and as an interactive web version. PharmVIP is a new integrated NGS workflow for the PGx community to facilitate discovery and clinical application.

The need to shift pharmacogenetic research from candidate gene to genome-wide association studies

The primary research approach in pharmacogenetics has been candidate gene association studies (CGAS), but pharmacogenomic genome-wide association studies (GWAS) are becoming more common. We are now at a critical juncture when the results of those two research approaches, CGAS and GWAS, can be compared in pharmacogenetics. We analyzed publicly available databases of pharmacogenetic CGAS and GWAS (i.e., the Pharmacogenomics Knowledgebase [PharmGKB^®] and the NHGRI-EBI GWAS catalog) and the vast majority of variants (98%) and genes (94%) discovered in pharmacogenomic GWAS were novel (i.e., not previously studied CGAS). Therefore, pharmacogenetic researchers are not selecting the right candidate genes in the vast majority of CGAS, highlighting a need to shift pharmacogenetic research efforts from CGAS to GWAS.

PharmaKU: A Web-Based Tool Aimed at Improving Outreach and Clinical Utility of Pharmacogenomics

With the tremendous advancements in genome sequencing technology in the field of pharmacogenomics, data have to be made accessible to be more efficiently utilized by broader clinical disciplines. Physicians who require the drug–genome interactome information, have been challenged by the complicated pharmacogenomic star-based classification system. We present here an end-to-end web-based pharmacogenomics tool, PharmaKU, which has a comprehensive easy-to-use interface. PharmaKU can help to overcome several hurdles posed by previous pharmacogenomics tools, including input in hg38 format only, while hg19/GRCh37 is now the most popular reference genome assembly among clinicians and geneticists, as well as the lack of clinical recommendations and other pertinent dosage-related information. This tool extracts genetic variants from nine well-annotated pharmacogenes (for which diplotype to phenotype information is available) from whole genome variant files and uses Stargazer software to assign diplotypes and apply prescribing recommendations from pharmacogenomic resources. The tool is wrapped with a user-friendly web interface, which allows for choosing hg19 or hg38 as the reference genome version and reports results as a comprehensive PDF document. PharmaKU is anticipated to enable bench to bedside implementation of pharmacogenomics knowledge by bringing precision medicine closer to a clinical reality.

Update on next generation sequencing of pharmacokinetics-related genes: Development of the PKseq panel, a platform for amplicon sequencing of drug-metabolizing enzyme and drug transporter genes

Genetic variation in pharmacokinetics (PK)-related genes encoding drug metabolizing enzymes or drug transporters is one of the most practical pharmacogenetic biomarkers for the prediction or explanation of an individual's response to drugs. Many pharmacogenomic variations are identified using targeted, whole-exome, and whole-genome sequencing, and the number of known novel variations and alleles in PK-related genes is increasing. The high homology of sequences among PK-related genes is suspected to lead to potential read misalignment and genotyping errors when short-read sequencing was performed. Therefore, highly efficient and accurate next generation sequencing (NGS) platforms for the sequencing of PK-related genes are needed. We have developed PKseq, a targeted sequencing panel based on multiplex PCR, which targets the coding regions of 37 drug transporters, 30 cytochrome P450 isoforms, 10 UDP-glucuronosyltransferases, 5 flavin-containing monooxygenases, 4 glutathione S-transferases, 4 sulfotransferases, and 10 other genes. In this review, we describe the current NGS platforms for the sequencing of PK-related genes. The NGS platforms, including the PKseq panel, will be useful not only for the identification of all the variants of PK-related genes associated with adverse drug reactions and drug efficacy, but also for clinical sequencing to achieve pharmacogenomics-based stratified medicine.

Coverage rate of ADME genes from commercial sequencing arrays

Pharmacogenomics offers remarkable potential for the rapid translation of discoveries into changes in clinical practice. In the present work, we are interested in evaluating the ability of commercially available genome-wide association sequencing chips to cover genes that have high pharmacogenomics potential.We used a set of 2794 variations within 369 absorption, distribution, metabolism, and elimination (ADME) genes of interest, as previously defined in collaboration with the Pharma ADME consortium. We have compared the Illumina TrueSeq and both Agilent SureSelect and HaloPlex sequencing technologies. We have developed Python scripts to evaluate the coverage for each of these products. In particular, we considered a specific list of 155 allelic variants in 34 genes which present high pharmacogenomics potential. Both the theoretical and practical coverage was assessed.Given the need to have a good coverage to establish confidently the functionality of an enzyme, the observed rates are unlikely to provide sufficient evidence for pharmacogenomics studies. We assessed the coverage using enrichment technology for exome sequencing using the Illumina Trueseq exome, Agilent SureSelectXT1 V4 and V5, and Haloplex exome, which offer a coverage of 96.12%, 91.61%, and 88.38%, respectively.Although pharmacogenomic advances had been limited in the past due in part to the lack of coverage of commercial genotyping chips, it is anticipated that future studies that make use of new sequencing technologies should offer a greater potential for discovery.

Pharmacogenetic content of commercial genome-wide genotyping arrays

Aim: We have evaluated the pharmacogenetic content of commercial human genome-wide genotyping arrays, as it is a critical determinant to enabling pharmacogenomic discoveries. Methods: Using bioinformatics approaches, we assessed 27,811 genetic variants in 3146 genes for their presence in 18 Illumina and 15 Affymetrix genome-wide arrays. Results: The pharmacogenetic content of the arrays varied greatly. The combination of the Affymetrix precision medicine array and PharmacoScan arrays (Affymetrix) had the highest coverage for a set of clinically actionable absorption, distribution, metabolism and excretion (ADME) variants, single nucleotide ADME variants and ADME insertions/deletions, with a physical coverage of 125/130 (96.2%), 9924/24,138 (41.1%) and 2252/3994 (56.4%), respectively. Conclusion: The combination of the Affymetrix precision medicine array and PharmacoScan arrays provided both genome-wide and pharmacogene coverage, which is crucial in the discovering of new variants responsible for drug adverse effects. These results will help in the design of pharmacogenomic studies and will enable a critical review of results from past studies.

DMET™ (Drug Metabolism Enzymes and Transporters): a pharmacogenomic platform for precision medicine

In the era of personalized medicine, high-throughput technologies have allowed the investigation of genetic variations underlying the inter-individual variability in drug pharmacokinetics/pharmacodynamics. Several studies have recently moved from a candidate gene-based pharmacogenetic approach to genome-wide pharmacogenomic analyses to identify biomarkers for selection of patient-tailored therapies. In this aim, the identification of genetic variants affecting the individual drug metabolism is relevant for the definition of more active and less toxic treatments. This review focuses on the potentiality, reliability and limitations of the DMET™ (Drug Metabolism Enzymes and Transporters) Plus as pharmacogenomic drug metabolism multi-gene panel platform for selecting biomarkers in the final aim to optimize drugs use and characterize the individual genetic background.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

Genetic epidemiology of pharmacogenetic variants in South East Asian Malays using whole-genome sequences

Expanding the scope of pharmacogenomic research by including multiple global populations is integral to building robust evidence for its clinical translation. Deep whole-genome sequencing of diverse ethnic populations provides a unique opportunity to study rare and common pharmacogenomic markers that often vary in frequency across populations. In this study, we aim to build a diverse map of pharmacogenetic variants in South East Asian (SEA) Malay population using deep whole-genome sequences of 100 healthy SEA Malay individuals. We investigated the allelic diversity of potentially deleterious pharmacogenomic variants in SEA Malay population. Our analysis revealed 227 common and 466 rare potentially functional single nucleotide variants (SNVs) in 437 pharmacogenomic genes involved in drug metabolism, transport and target genes, including 74 novel variants. This study has created one of the most comprehensive maps of pharmacogenetic markers in any population from whole genomes and will hugely benefit pharmacogenomic investigations and drug dosage recommendations in SEA Malays.

An integrated analysis tool for analyzing hybridization intensities and genotypes using new-generation population-optimized human arrays

BACKGROUND

Affymetrix Axiom single nucleotide polymorphism (SNP) arrays provide a cost-effective, high-density, and high-throughput genotyping solution for population-optimized analyses. However, no public software is available for the integrated genomic analysis of hybridization intensities and genotypes for this new-generation population-optimized genotyping platform.

RESULTS

A set of statistical methods was developed for an integrated analysis of allele frequency (AF), allelic imbalance (AI), loss of heterozygosity (LOH), long contiguous stretch of homozygosity (LCSH), and copy number variation or alteration (CNV/CNA) on the basis of SNP probe hybridization intensities and genotypes. This study analyzed 3,236 samples that were genotyped using different SNP platforms. The proposed AF adjustment method considerably increased the accuracy of AF estimation. The proposed quick circular binary segmentation algorithm for segmenting copy number reduced the computation time of the original segmentation method by 30-67 %. The proposed CNV/CNA detection, which integrates AI and LOH/LCSH detection, had a promising true positive rate and well-controlled false positive rate in simulation studies. Moreover, our real-time quantitative polymerase chain reaction experiments successfully validated the CNVs/CNAs that were identified in the Axiom data analyses using the proposed methods; some of the validated CNVs/CNAs were not detected in the Affymetrix Array 6.0 data analysis using the Affymetrix Genotyping Console. All the analysis functions are packaged into the ALICE (AF/LOH/LCSH/AI/CNV/CNA Enterprise) software.

CONCLUSIONS

ALICE and the used genomic reference databases, which can be downloaded from http://hcyang.stat.sinica.edu.tw/software/ALICE.html , are useful resources for analyzing genomic data from the Axiom and other SNP arrays.

Emerging applications of metabolomics in drug discovery and precision medicine

Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines

Variation in the expression level and activity of genes involved in drug disposition and action (‘pharmacogenes') can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-sequencing (RNA-seq) to determine the expression levels and alternative splicing of 389 Pharmacogenomics Research Network pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines, which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from 139 different individuals across the 5 tissues (20–45 individuals per tissue type) revealed substantial variation in both expression levels and splicing across samples and tissue types. Comparison with GTEx data yielded a consistent picture. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.

CYP2A6 genotyping methods and strategies using real-time and end point PCR platforms

CYP2A6 genotyping is of clinical importance--CYP2A6 gene variants influence nicotine metabolism and are associated with nicotine dependence, cigarettes per day, smoking cessation and the risk for tobacco-associated cancers. CYP2A6 gene variants also influence the metabolism of therapeutic drugs, such as the anticancer agents, tegafur and letrozole. Over the years, CYP2A6 genotyping methods have evolved to incorporate novel gene variants and to circumvent genotyping errors resulting from the high degree of homology between CYP2A6 and neighboring CYP2A genes. Herein, CYP2A6 genotyping strategies are described for commonly genotyped functionally significant alleles including SNPs, small insertions/deletions and more complex structural variants. The methods presented utilize higher throughput SYBR green real-time PCR technology in addition to standard thermocycling.

Personalized pharmacogenomics profiling using whole-genome sequencing

AIM

Pharmacogenomics holds promise to rationalize drug use by minimizing drug toxicity and at the same time increase drug efficacy. There are currently several assays to screen for known pharmacogenomic biomarkers for the most commonly prescribed drugs. However, these genetic screening assays cannot account for other known or novel pharmacogenomic markers.

MATERIALS & METHODS

We analyzed whole-genome sequences of 482 unrelated individuals of various ethnic backgrounds to obtain their personalized pharmacogenomics profiles.

RESULTS

Bioinformatics analysis revealed 408,964 variants in 231 pharmacogenes, from which 26,807 were residing on exons and proximal regulatory sequences, whereas 16,487 were novel. In silico analyses indicated that 1012 novel pharmacogene-related variants possibly abolish protein function. We have also performed whole-genome sequencing analysis in a seven-member family of Greek origin in an effort to explain the variable response rate to acenocoumarol treatment in two family members.

CONCLUSION

Overall, our data demonstrate that whole-genome sequencing, unlike conventional genetic screening methods, is necessary to determine an individual's pharmacogenomics profile in a more comprehensive manner, which, combined with the gradually decreasing whole-genome sequencing costs, would expedite bringing personalized medicine closer to reality.

Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study

BACKGROUND

Understanding of human variation in toxicity to environmental chemicals remains limited, so human health risk assessments still largely rely on a generic 10-fold factor (10½ each for toxicokinetics and toxicodynamics) to account for sensitive individuals or subpopulations.

OBJECTIVES

We tested a hypothesis that population-wide in vitro cytotoxicity screening can rapidly inform both the magnitude of and molecular causes for interindividual toxicodynamic variability.

METHODS

We used 1,086 lymphoblastoid cell lines from the 1000 Genomes Project, representing nine populations from five continents, to assess variation in cytotoxic response to 179 chemicals. Analysis included assessments of population variation and heritability, and genome-wide association mapping, with attention to phenotypic relevance to human exposures.

RESULTS

For about half the tested compounds, cytotoxic response in the 1% most "sensitive" individual occurred at concentrations within a factor of 10½ (i.e., approximately 3) of that in the median individual; however, for some compounds, this factor was > 10. Genetic mapping suggested important roles for variation in membrane and transmembrane genes, with a number of chemicals showing association with SNP rs13120371 in the solute carrier SLC7A11, previously implicated in chemoresistance.

CONCLUSIONS

This experimental approach fills critical gaps unaddressed by recent large-scale toxicity testing programs, providing quantitative, experimentally based estimates of human toxicodynamic variability, and also testable hypotheses about mechanisms contributing to interindividual variation.

Challenges to integrating pharmacogenetic testing into medication therapy management

Some have proposed the integration of pharmacogenetic (PGx) testing into medication therapy management (MTM) to enable further refinement of treatments to reduce risk of adverse responses and improve efficacy. PGx testing involves the analysis of genetic variants associated with therapeutic or adverse response and may be useful in enhancing the ability to identify ineffective and/or harmful drugs or drug combinations. This "enhanced" MTM might also reduce patient concerns about side effects and increase confidence that the medication is effective, addressing 2 key factors that impact patient adherence: concern and necessity. However, the feasibility and effectiveness of the integration of PGx testing into MTM in clinical practice has not yet been determined. In this commentary, we consider some of the challenges to the integration and delivery of PGx testing in MTM services.

Whole genome sequencing in pharmacogenomics

Pharmacogenomics aims to shed light on the role of genes and genomic variants in clinical treatment response. Although, several drug-gene relationships are characterized to date, many challenges still remain toward the application of pharmacogenomics in the clinic; clinical guidelines for pharmacogenomic testing are still in their infancy, whereas the emerging high throughput genotyping technologies produce a tsunami of new findings. Herein, the potential of whole genome sequencing on pharmacogenomics research and clinical application are highlighted.

Pharmacogenomics in children: advantages and challenges of next generation sequencing applications

Pharmacogenetics is considered as a prime example of how personalized medicine nowadays can be put into practice. However, genotyping to guide pharmacological treatment is relatively uncommon in the routine clinical practice. Several reasons can be found why the application of pharmacogenetics is less than initially anticipated, which include the contradictory results obtained for certain variants and the lack of guidelines for clinical implementation. However, more reproducible results are being generated, and efforts have been made to establish working groups focussing on evidence-based clinical guidelines. For another pharmacogenetic hurdle, the speed by which a pharmacogenetic profile for a certain drug can be obtained in an individual patient, there has been a revolution in molecular genetics through the introduction of next generation sequencing (NGS), making it possible to sequence a large number of genes up to the complete genome in a single reaction. Besides the enthusiasm due to the tremendous increase of our sequencing capacities, several considerations need to be made regarding quality and interpretation of the sequence data as well as ethical aspects of this technology. This paper will focus on the different NGS applications that may be useful for pharmacogenomics in children and the challenges that they bring on.

The future of warfarin pharmacogenetics in under-represented minority groups

Genotype-based dosing recommendations are provided in the US FDA-approved warfarin labeling. However, data that informed these recommendations were from predominately Caucasian populations. Studies show that variants contributing to warfarin dose requirements in Caucasians provide similar contributions to dose requirements in US Hispanics, but significantly lesser contributions in African-Americans. Further data demonstrate that variants occurring commonly in individuals of African ancestry, but rarely in other racial groups, significantly influence dose requirements in African-Americans. These data suggest that it is important to consider variants specific for African-Americans when implementing genotype-guided warfarin dosing in this population.

Polymorphisms of Phase I and Phase II Enzymes and Breast Cancer Risk

Breast cancer is a complex disease which is provoked by a multitude of exogenous and endogenous factors including genetic variations. Recent genome-wide association studies identified a set of more than 18 novel low penetrant susceptibility loci, however, a limitation of this powerful approach is the hampered analysis of polymorphisms in DNA sequences with a high degree of similarity to other genes or pseudo genes. Since this common feature affects the majority of the highly polymorphic genes encoding phase I and II enzymes the retrieval of specific genotype data requires adapted amplification methods. With regard to breast cancer these genes are of certain interest due to their involvement in the metabolism of carcinogens like exogenous genotoxic compounds or steroid hormones. The present review summarizes the observed effects of functional genetic variants of phase I and II enzymes in well designed case control studies to shed light on their contribution to breast cancer risk.

Omics and drug response

A new generation of technologies commonly named omics permits assessment of the entirety of the components of biological systems and produces an explosion of data and a major shift in our concepts of disease. These technologies will likely shape the future of health care. One aspect of these advances is that the data generated document the uniqueness of each human being in regard to disease risk and treatment response. These developments have reemphasized the concept of personalized medicine. Here we review the impact of omics technologies on one key aspect of personalized medicine: the individual drug response. We describe how knowledge of different omics may affect treatment decisions, namely drug choice and drug dose, and how it can be used to improve clinical outcomes.