Teri E. Klein

Estimation of the warfarin dose with clinical and pharmacogenetic data.

BACKGROUND Genetic variability among patients plays an important role in determining the dose of warfarin that should be used when oral anticoagulation is initiated, but practical methods of using genetic information have not been evaluated in a diverse and large population. We developed and used an algorithm for estimating the appropriate warfarin dose that is based on both clinical and genetic data from a broad population base. METHODS Clinical and genetic data from 4043 patients were used to create a dose algorithm that was based on clinical variables only and an algorithm in which genetic information was added to the clinical variables. In a validation cohort of 1009 subjects, we evaluated the potential clinical value of each algorithm by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual stable therapeutic dose; we also evaluated other clinically relevant indicators. RESULTS In the validation cohort, the pharmacogenetic algorithm accurately identified larger proportions of patients who required 21 mg of warfarin or less per week and of those who required 49 mg or more per week to achieve the target international normalized ratio than did the clinical algorithm (49.4% vs. 33.3%, P<0.001, among patients requiring < or = 21 mg per week; and 24.8% vs. 7.2%, P<0.001, among those requiring > or = 49 mg per week). CONCLUSIONS The use of a pharmacogenetic algorithm for estimating the appropriate initial dose of warfarin produces recommendations that are significantly closer to the required stable therapeutic dose than those derived from a clinical algorithm or a fixed-dose approach. The greatest benefits were observed in the 46.2% of the population that required 21 mg or less of warfarin per week or 49 mg or more per week for therapeutic anticoagulation.

Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

Clinical assessment incorporating a personal genome

BACKGROUND The cost of genomic information has fallen steeply, but the clinical translation of genetic risk estimates remains unclear. We aimed to undertake an integrated analysis of a complete human genome in a clinical context. METHODS We assessed a patient with a family history of vascular disease and early sudden death. Clinical assessment included analysis of this patients full genome sequence, risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome and clinical risk. Disease and risk analysis focused on prediction of genetic risk of variants associated with mendelian disease, recognised drug responses, and pathogenicity for novel variants. We queried disease-specific mutation databases and pharmacogenomics databases to identify genes and mutations with known associations with disease and drug response. We estimated post-test probabilities of disease by applying likelihood ratios derived from integration of multiple common variants to age-appropriate and sex-appropriate pre-test probabilities. We also accounted for gene-environment interactions and conditionally dependent risks. FINDINGS Analysis of 2.6 million single nucleotide polymorphisms and 752 copy number variations showed increased genetic risk for myocardial infarction, type 2 diabetes, and some cancers. We discovered rare variants in three genes that are clinically associated with sudden cardiac death-TMEM43, DSP, and MYBPC3. A variant in LPA was consistent with a family history of coronary artery disease. The patient had a heterozygous null mutation in CYP2C19 suggesting probable clopidogrel resistance, several variants associated with a positive response to lipid-lowering therapy, and variants in CYP4F2 and VKORC1 that suggest he might have a low initial dosing requirement for warfarin. Many variants of uncertain importance were reported. INTERPRETATION Although challenges remain, our results suggest that whole-genome sequencing can yield useful and clinically relevant information for individual patients. FUNDING National Institute of General Medical Sciences; National Heart, Lung And Blood Institute; National Human Genome Research Institute; Howard Hughes Medical Institute; National Library of Medicine, Lucile Packard Foundation for Childrens Health; Hewlett Packard Foundation; Breetwor Family Foundation.

Pharmacogenomics Knowledge for Personalized Medicine

The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene–drug associations and genotype–phenotype relationships. Curators assign levels of evidence to variant–drug associations using well‐defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high‐quality information supporting personalized medicine–implementation projects.

Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C19 Genotype and Clopidogrel Therapy: 2013 Update

Cytochrome P450 (CYP)2C19 catalyzes the bioactivation of the antiplatelet prodrug clopidogrel, and CYP2C19 loss‐of‐function alleles impair formation of active metabolites, resulting in reduced platelet inhibition. In addition, CYP2C19 loss‐of‐function alleles confer increased risks for serious adverse cardiovascular (CV) events among clopidogrel‐treated patients with acute coronary syndromes (ACSs) undergoing percutaneous coronary intervention (PCI). Guideline updates include emphasis on appropriate indication for CYP2C19 genotype–directed antiplatelet therapy, refined recommendations for specific CYP2C19 alleles, and additional evidence from an expanded literature review (updates at http://www.pharmgkb.org).

CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network

The slow rate at which pharmacogenetic tests are being adopted in clinical practice is partly due to the lack of specific guidelines on how to adjust medications on the basis of the genetic test results. One of the goals of the Clinical Pharmacogenetics Implementation Consortium (CPIC) of the National Institutes of Healths Pharmacogenomics Research Network (http://www.pgrn.org) and the Pharmacogenomics Knowledge Base (PharmGKB, http://www.pharmgkb.org) is to provide peer‐reviewed, updated, evidence‐based, freely accessible guidelines for gene/drug pairs. These guidelines will facilitate the translation of pharmacogenomic knowledge from bench to bedside.

Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 Genotypes and Warfarin Dosing

Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the dose required to achieve target anticoagulation. Common genetic variants in the cytochrome P450–2C9 (CYP2C9) and vitamin K–epoxide reductase complex (VKORC1) enzymes, in addition to known nongenetic factors, account for ~50% of warfarin dose variability. The purpose of this article is to assist in the interpretation and use of CYP2C9 and VKORC1 genotype data for estimating therapeutic warfarin dose to achieve an INR of 2–3, should genotype results be available to the clinician. The Clinical Pharmacogenetics Implementation Consortium (CPIC) of the National Institutes of Health Pharmacogenomics Research Network develops peer–reviewed gene–drug guidelines that are published and updated periodically on http://www.pharmgkb.org based on new developments in the field. 1

Clinical Pharmacogenetics Implementation Consortium guidelines for thiopurine methyltransferase genotype and thiopurine dosing.

Thiopurine methyltransferase (TPMT) activity exhibits monogenic co‐dominant inheritance, with ethnic differences in the frequency of occurrence of variant alleles. With conventional thiopurine doses, homozygous TPMT‐deficient patients (~1 in 178 to 1 in 3,736 individuals with two nonfunctional TPMT alleles) experience severe myelosuppression, 30–60% of individuals who are heterozygotes (~3–14% of the population) show moderate toxicity, and homozygous wild‐type individuals (~86–97% of the population) show lower active thioguanine nucleolides and less myelosuppression. We provide dosing recommendations (updates at http://www.pharmgkb.org) for azathioprine, mercaptopurine (MP), and thioguanine based on TPMT genotype.

Integrating genotype and phenotype information : an overview of the pharmGKB project

Pharmacogenetics seeks to explain how people respond in different ways to the same drug treatment. A classic example of the importance of pharmacogenomics is the variation in individual responses to the anti-leukemia drug, 6-mercaptopurine. Most people metabolize the drug quickly. Some individuals, with a genetic variation for the enzyme thiopurine methyltransferase (TPMT),1 do not. Consequently, they need lower doses of 6-mercaptopurine for effective treatment as normal doses can be lethal. One of the many promises of the human genome project is an ability to pharmacologically treat individuals in a more personalized rather than statistical manner.

Doxorubicin pathways: pharmacodynamics and adverse effects.

The goal of this study is to give a brief background on the literature supporting the PharmGKB pathway about doxorubicin action, and provides a summary of this active area of research. The reader is referred to recent in-depth reviews [1–4] for more detailed discussion of this important and complex pathway. Doxorubicin is an anthracyline drug first extracted from Streptomyces peucetius var. caesius in the 1970’s and routinely used in the treatment of several cancers including breast, lung, gastric, ovarian, thyroid, non-Hodgkin’s and Hodgkin’s lymphoma, multiple myeloma, sarcoma, and pediatric cancers [5–7]. A major limitation for the use of doxorubicin is cardiotoxicity, with the total cumulative dose being the only criteria currently used to predict the toxicity [4,8]. As there is evidence that the mechanisms of anticancer action and of cardiotoxicity occur through different pathways there is hope for the development of anthracycline drugs with equal efficacy but reduced toxicity [4]. Knowledge of the pharmacogenomics of these pathways may eventually allow for future selection of patients more likely to achieve efficacy at lower doses or able to withstand higher doses with lesser toxicity. We present here graphical representations of the candidate genes for the pharmacogenomics of doxorubicin action in a stylized cancer cell (Fig. 1) and toxicity in cardiomyocytes (Fig. 2), and a table describing the key variants examined so far. Open in a separate window Fig. 1 Graphical representation of the candidate genes involved in the pharmacodynamics of doxorubicin in a stylized cancer cell. A fully interactive version of this pathway is available online at PharmGKB at http://www.pharmgkb.org/do/serve?objId=PA165292163o ROS, reactive oxygen species.