Robert R. Freimuth

A proposed nomenclature system for the cytosolic sulfotransferase (SULT) superfamily.

A nomenclature system for the cytosolic sulfotransferase (SULT) superfamily has been developed. The nomenclature guidelines were applied to 65 SULT cDNAs and 18 SULT genes that were characterized from eukaryotic organisms. SULT cDNA and gene sequences were identified by querying the GenBank databases and from published reports of their identification and characterization. These sequences were evaluated and named on the basis of encoded amino acid sequence identity and, in a few cases, a necessity to maintain historical naming convention. Family members share at least 45% amino acid sequence identity whereas subfamily members are at least 60% identical. cDNAs which encode amino acid sequences of at least 97% identity to each other were assigned identical isoform names. We also attempted to categorize orthologous enzymes between various species, where these have been identified, and the nomenclature includes a species descriptor. We present recommendations for the naming of allelic variants of SULT genes and their derived allozymes arising from single nucleotide polymorphisms and other genetic variation. The superfamily currently comprises 47 mammalian SULT isoforms, one insect isoform and eight plant enzymes, and collectively these sequences represent nine separate SULT families and 14 subfamilies. It is hoped that this nomenclature system will be widely adopted and that, as novel SULTs are identified and characterized, investigators will name their discoveries according to these guidelines.

Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine’s Standards for Developing Trustworthy Clinical Practice Guidelines.

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol.

OBJECTIVE To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). PATIENTS AND METHODS We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. RESULTS The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

The pharmacogenomics research network translational pharmacogenetics program: Overcoming challenges of real-world implementation

The pace of discovery of potentially actionable pharmacogenetic variants has increased dramatically in recent years. However, the implementation of this new knowledge for individualized patient care has been slow. The Pharmacogenomics Research Network (PGRN) Translational Pharmacogenetics Program seeks to identify barriers and develop real‐world solutions to implementation of evidence‐based pharmacogenetic tests in diverse health‐care settings. Dissemination of the resulting toolbox of “implementation best practices” will prove useful to a broad audience.

Development and use of active clinical decision support for preemptive pharmacogenomics

Background Active clinical decision support (CDS) delivered through an electronic health record (EHR) facilitates gene-based drug prescribing and other applications of genomics to patient care. Objective We describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively. Materials and methods Clinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patients EHR, typically before a relevant drug is prescribed. Problem list entries created for high-risk phenotypes provide an unambiguous trigger for delivery of post-test alerts to clinicians when high-risk drugs are prescribed. In addition, pre-test alerts are issued if a very-high risk medication is prescribed (eg, a thiopurine), prior to the appropriate pharmacogenetic test result being entered into the EHR. Our CDS can be readily modified to incorporate new genes or high-risk drugs as they emerge. Results Through November 2012, 35 customized pharmacogenetic rules have been implemented, including rules for TPMT with azathioprine, thioguanine, and mercaptopurine, and for CYP2D6 with codeine, tramadol, amitriptyline, fluoxetine, and paroxetine. Between May 2011 and November 2012, the pre-test alerts were electronically issued 1106 times (76 for thiopurines and 1030 for drugs metabolized by CYP2D6), and the post-test alerts were issued 1552 times (1521 for TPMT and 31 for CYP2D6). Analysis of alert outcomes revealed that the interruptive CDS appropriately guided prescribing in 95% of patients for whom they were issued. Conclusions Our experience illustrates the feasibility of developing computational systems that provide clinicians with actionable alerts for gene-based drug prescribing at the point of care.

Human sulfotransferase SULT2A1 pharmacogenetics: genotype-to-phenotype studies

SULT2A1 catalyzes the sulfate conjugation of dehydroepiandrosterone (DHEA) as well as other steroids. As a step toward pharmacogenetic studies, we have ‘resequenced’ SULT2A1 using 60 DNA samples from African-American and 60 samples from Caucasian-American subjects. All exons, splice junctions and approximately 370 bp located 5′ of the site of transcription initiation were sequenced. We observed 15 single nucleotide polymorphisms (SNPs), including three non-synonymous coding SNPs (cSNPs) that were present only in DNA from African-American subjects. Linkage analysis revealed that two of the nonsynonymous cSNPs were tightly linked. Expression constructs were created for all nonsynonymous cSNPs observed, including a ‘double variant’ construct that included the two linked cSNPs, and those constructs were expressed in COS-1 cells. SULT2A1 activity was significantly decreased for three of the four variant allozymes. Western blot analysis demonstrated that decreased levels of immunoreactive protein appeared to be the major mechanism responsible for decreases in activity, although apparent Km values also varied among the recombinant allozymes. In addition, the most common of the nonsynonymous cSNPs disrupted the portion of SULT2A1 involved with dimerization, and this variant allozyme behaved as a monomer rather than a dimer during gel filtration chromatography. These observations indicate that common genetic polymorphisms for SULT2A1 can result in reductions in levels of both activity and enzyme protein. They also raise the possibility of ethnic-specific pharmacogenetic variation in SULT2A1-catalyzed sulfation of both endogenous and exogenous substrates for this phase II drug-metabolizing enzyme.

Human cytosolic sulfotransferase database mining: identification of seven novel genes and pseudogenes

ABSTRACTA total of 10 SULT genes are presently known to be expressed in human tissues. We performed a comprehensive genome-wide search for novel SULT genes using two different but complementary approaches, and developed a novel graphical display to aid in the annotation of the hits. Seven novel human SULT genes were identified, five of which were predicted to be pseudogenes, including two processed pseudogenes and three pseudogenes that contained introns. Those five pseudogenes represent the first unambiguous SULT pseudogenes described in any species. Expression-profiling studies were conducted for one novel gene, SULT6B1, and a series of alternatively spliced transcripts were identified in the human testis. SULT6B1 was also present in chimpanzee and gorilla, differing at only seven encoded amino-acid residues among the three species. The results of these database mining studies will aid in studies of the regulation of these SULT genes, provide insights into the evolution of this gene family in humans, and serve as a starting point for comparative genomic studies of SULT genes.

Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

Introduction:Reporting and sharing pharmacogenetic test results across clinical laboratories and electronic health records is a crucial step toward the implementation of clinical pharmacogenetics, but allele function and phenotype terms are not standardized. Our goal was to develop terms that can be broadly applied to characterize pharmacogenetic allele function and inferred phenotypes.Materials and methods:Terms currently used by genetic testing laboratories and in the literature were identified. The Clinical Pharmacogenetics Implementation Consortium (CPIC) used the Delphi method to obtain a consensus and agree on uniform terms among pharmacogenetic experts.Results:Experts with diverse involvement in at least one area of pharmacogenetics (clinicians, researchers, genetic testing laboratorians, pharmacogenetics implementers, and clinical informaticians; n = 58) participated. After completion of five surveys, a consensus (>70%) was reached with 90% of experts agreeing to the final sets of pharmacogenetic terms.Discussion:The proposed standardized pharmacogenetic terms will improve the understanding and interpretation of pharmacogenetic tests and reduce confusion by maintaining consistent nomenclature. These standard terms can also facilitate pharmacogenetic data sharing across diverse electronic health care record systems with clinical decision support.Genet Med 19 2, 215–223.

SNP interaction detection with Random Forests in high-dimensional genetic data

BackgroundIdentifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.ResultsRF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.ConclusionsWhile RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

Clinical Pharmacogenetics Implementation Consortium Guidelines for HLA-B Genotype and Abacavir Dosing: 2014 Update

The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA‐B Genotype and Abacavir Dosing were originally published in April 2012. We reviewed recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplementary Material online and included additional resources for applying CPIC guidelines to the electronic health record. Up‐to‐date information can be found at PharmGKB (http://www.pharmgkb.org).