THE STAR ALLELE NOMENCLATURE: HOW IT WORKS

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The variations in the cytochrome P450 genes (CYP) lead to four different metabolizer phenotypes: ultrarapid, extensive, intermediate and poor.

A patient with a ultrarapid drug metabolism bears double or multiple copies of an allele with normal or increased functionality, whereas patients with intermediate or poor drug metabolism bear one or more alleles with reduced functionality (these alleles are typically consistent with inactivating mutations or large gene deletions). The term extensive metabolizer is used instead to describe those individuals with two standard copies of the normally functional allele. Extensive metabolizers are therefore carrying the wild-type allele, also called consensus allele, which corresponds to the allele *1 in the star allele nomenclature. The numbers *2, *3, *4 and so on represent alleles with altered functionality which may lead to profiles of increased or reduced drug metabolism.

By using one single star allele one can identify not just a single variant, but even a group of variants. Rules are as follows:

A) to distinguish an allele of proved pharmacogenomic relevance from a group of alleles where this allele is segregating together with other neutral alleles, numbers and letters are used.

Example: the symbol CYP2B6*4A identifies the pharmacogenomic relevant allele K262R in the CYP2B6 gene, whereas the symbol CYP2B6*4B identifies the combination of the K262R allele with one or more of the polymorphisms which can be easily detected in association with it: -2320T>C; -1778A>G; -1186C>G; -750T>C; 18053A>G. In both cases (CYP2B6*4A and CYP2B6*4B) the pharmacogenomic effect remains the one of K262R.

B) similarly, to identify group of functional alleles, the number of the most "powerful" allele is always used, followed by a letter.

Example: the symbol CYP2C19*2 identifies the splice mutation 681G>A, which reduces the protein functionality. Other variants which also show some functional effect can often segregate in association with this splice mutation. However, the effect of the associated variants is always less relevant than the effect exerted by the splice mutation 681G>A. So different groups of associated alleles can be represented as follows:

CYP2C19*2A: 99C>T; 681G>A; 990C>T; 991A>G

CYP2C192*B: 99C>T; 276G>C; 681G>A; 990C>T; 991A>G

CYP2C19*C: 99C>T; 481G>C; 681G>A; 990C>T; 991A>G

In all cases, the final pharmacogenomic effect which can be observed at the clinical level is always the one given by the splice mutation 681G>A.

Of note, the first alleles described in the CYP genes family did not follow these rules exactly, nevertheless their original nomenclature is still used for simplicity.

STAR ALLELE NOMENCLATURE: INTRODUCTION
PHARMACOGENOMICS: MAIN PAGE

THE STAR ALLELE NOMENCLATURE IN PHARMACOGENOMICS

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In pharmacogenomics it is very common to identify genetic variants with a special nomenclature, which is not elsewhere used in genetics. It is the so-called star allele nomenclature. In this nomenclature, alleles with a pharmacogenomic relevance aren't identified with their cDNA or genomic position as usual (HGVS nomenclature), but through the means of numbers and letters separated from the gene name by a star (that's why this is called star allele nomenclature). For example: CYP3A5*2 identifies the genetic variant in genomic position g.27289C>A leading to the amino acid substitution p.T398N in the protein chain. The star allele nomenclature is therefore useful to identify a certain allele in a fast and easy way, whereas the standard HGVS nomenclature could be less comprehensible (and more subject to transcription mistakes) by not specialized personnel. 

The star allele nomenclature has been firstly used to identify alleles within the cytochrome P450 (CYP) gene family. From that it spread to all genes that have been studied in pharmacogenomics. Of note, the star allele nomenclature is used only do identify pharmacogenomic markers and it is not elsewhere used in genetics. 

The list of the star alleles of the cytochrome P450 gene family and of the POR gene is available at http://www.cypalleles.ki.se.

However the star allele nomenclature has some defects. First of all it does not indicate the mutation type, i.e. if it is a missense, nonsense or splice mutation or again a large deletion/duplication. Moreover, despite the possibility it has to identify even a group of alleles under one single number (to know how the star allele nomenclature works you can read here), it is not flexible enough to express the complexity and the enormous amount of data as produced by high-throughput sequencing (e.g exome sequencing and genome sequencing). Nevertheless the star allele nomenclature remains the gold-standard in the identification of pharmacogenomic markers in the scientific literature and in the official documents of the drug agencies.

STAR ALLELE NOMENCLATURE: HOW IT WORKS

PHARMACOGENOMICS: INTRODUCTION

COMMERCIAL KITS FOR PHARMACOGENOMIC TESTING

Some commercial kits are already existing which can help any lab to do the screening of relevant pharmacogenetic variants known today. One of these is for instance the DMET™ Plus Solution kit by Affimetrix, which covers 1,936 genetic variants across 231 relevant genes.

Another commercial kit available for pharmacogenomic testing is the VeraCode ADME Core Panel by Illumina, which interrogates variants in 34 genes. This panel is based on the recommendation of PharmaADME working group, consisting of industry and academic experts who has developed a list of pharmacogenetic markers.

Another kit offering the screening of the ADME genes as proposed by the PharmaADME working group is the iPLEX® ADME PGx Pro Panel provided by Agena Bioscience.

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CLINICAL VALIDITY OF PHARMACOGENOMIC TESTING

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PHARMACOGENOMIC DATABASES

PHARMACOGENOMIC DATABASES

The best pharmacogenomic database today available is PharmGKB. At PharmGKB website it is also possible to download the current guidelines on selected gene variants as edited by the Clinical Pharmacogenetics Implementation Consortium (CPIC).

It’s not a database, but it’s worth to be highlighted for its activity of research and collaboration funding: it’s the PharmacoGenomics Reasearch Network (PGRN), supported by the National Institute of Health (NIH) since 2000.

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PHARMACOGENOMICS AND POPULATION DIVERSITY

Because of population genetic diversity, pharmacogenomics correlations are significantly varying from an ethnicity to another one. It happens so that a pharmacogenetic test shows to be of great epidemiological relevance within a certain geographical area, whereas it is of lower impact in some other ones. For instance, the variant 3673A of the VKORC1 gene, an allele to be considered in warfarin dosage, is rare in sub-Saharian Africans (less than 10%) but it is found at an extremely higher frequency in Southeast Asian populations (more than 90%). The Pharamcogenomics for Every Nation Initiative (PGENI) has exactly the aim of characterizing such pharmacogenomics differences among various populations. An online resource dedicated to multi-ethnic frequency data for pharmacogenetically relevant single nucleotide polymorphisms is FINDbase-PGx. Such data are also summarized at PharmaGKB website.

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GENETIC VARIANTS OF PHARMACOGENOMIC RELEVANCE

Many pharmacogenetically important genes belong to the family of the cytochrome P450, solute carrier (SLC), ATP-binding cassette (ABC), aldehyde dehydrogenase (ALDH) and UDP-glucoronyltransferase (UGT) families.

The first (and one of the more important) gene involved in pharmacogenomic studies is CYP2D6. It is believed that the enzyme encoded by this gene (cytochrome P450-2D6) is involved in the metabolism of about 25% of drugs available today. So far, more than 100 CYP2D6 alleles have been reported to be of pharmacogenomic relevance. In the last years CYP2D6 genotyping has been elaborated to classify patients into four metabolizer phenotypes: ultrarapid, extensive, intermediate and poor.

Many other cytochrome P450 genes like CYP2C9 and CYP2C19 have been pharmacogenetically characterized. CYP2C9 alleles, along with VKORC1 and CYP4F2 alleles, are important in warfarin efficacy and dosing. The CYP2C19*2 allele has been associated with a lower production of active metabolites of clopidrogel, higher platelet aggregation ratio and adverse clinical outcome. Other notable associations of these two genes are with phenytoin, acenocoumarol, glibenclamide, gliclazide, glimepiride, phenprocoumon, and tolbutamide (CYP2C9) and with carisoprodol, citalopram, clobazam, clopidogrel, dexlansoprazole, diazepam, esomeprazole, lansoprazole, nelfinavir, omeprazole, pantoprazole, prasugrel, rabeprazole, drospirenone ethinyl estradiol, atazanavir, axitinib, ticagrelor and voriconazole (CYP2C19).

Other important pharmacogenomic markers have been identified in the following gene/drug associations: ABCB1/aliskiren, CYP2B6/efavirenz, CYP34A/ticagrelor, aripiprazole, cabazitaxel, darunavir, dronedarone, fosamprenavir, gefitinib, indinavir, ivabradine, nelfinavir, posaconazole, ritonavir, ruxolitinib, sirolimus, sunitinib, telithromycin, tipranavir, voriconazole, zonisamide, CYP3A5/tacrolimus, DPYD/tegafur, capecitabine, fluorouracil, NAT2/isosorbide dinitrate and hydralazine hydrochloride, rifampin-isoniazid-pyrazinamid, SLC22A2/fampridine, SLCO1B1/simvastatin, TPMT/azathioprine, mercaptopurine, thioguanine, azathioprine, UGT1A1/irinotecan, IFNL3/PEG-interpheron-α (PEG-IFN alpha).

A working group of experts from the academy and the industry has actually collected the information on the most important genes so far recognized to be of relevance for their pharmacogenomic correlation. This is the PharmaADME working group, whose list have been used also to design commercial kits for genetic testing (the ADME gene list).

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CLINICAL VALIDITY OF PHARMACOGENOMIC TESTING

In genetic testing, clinical validity can be defined as the ability to predict a phenotype associated with a genotype. For typical genetic diseases (i.e. chromosomal aberrations and Mendelian disorders) clinical validity is approaching 100%, since there is a direct cause-effect relation between a certain genetic mutation and the presence of the disease.

Much different are things for what concerns pharmacogenomics since the scientific evidence of the association between certain genetic variant(s) and the response to a drug can be sometime controversial. It can happen so that a genetic variant found to be significantly associated with high sensitivity to a certain treatment in one study, is found to be of no pharmacogenomic relevance in another reseach. In extreme cases a certain genetic variant is found to exert a total opposite effect from another author.  This issue of controversial evidence is still a major obstacle to the diffusion of pharmacogenetic testing in daily routine since it mostly affects the trust of health professionals in pharmacogenomic solutions.

However, it must be said that in certain cases pharmacogenomic correlations have been well characterized and replicated by several independent studies. In such cases the pharmacogenomic evidence has been coded into clinical practice guidelines by known working groups of specialists (see for instance the guidelines from the Clinical Pharmacogenetics Implementation Consortium - CPIC, www.pharmagkb.org/page/cipc, the Royal Dutch Association for the Advancement of Pharmacy-Pharmacogenetics Working Group – KNMP-PWG, the EGAPP working group or the ACMG). By following these guidelines, health care operators can confidently take decisions in drug selection and dose adjustment.

An example of pharmacogenomic evidence successfully confirmed and applied in daily clinical practice is the one between the allele B*5701 in the HLA-B gene and the susceptibility to severe hypersensitivity reaction to abacavir treatment in HIV-positive patients. This pharmacogenetic test is now routinely performed to help infectivologists in selecting the most appropriate treatment combination for HIV-positive patients.

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PHARMACOGENOMICS

Pharmacogenomics (sometimes referred to also as pharmacogenetics) is a discipline aimed to identify human genetic traits which can be associated either to drug response (sensitivity/resistance) or to adverse drug reactions. Some individuals have certain genetic variants (also called pharmacogenetic or pharmacogenomic markers) which can affect the way a drug is metabolized and therefore influence the drug power and/or the appearance of side-effects. Therefore pharmacogenomics is essentially relevant for (1) dose adjustment and (2) drug selection.

Until yesterday, the costs and turn-around time of testing, a lack of skilled counselling staff and regulatory and reimbursement issues curbed the spreading of pharmacogenetic testing. Today, thanks to time-effective analyses and preventive genotyping programs, pharmacogenomics is about to become a reality in the context of the so-called personalized medicine.

Personalized medicine is not something new. Already in ancient Greece Hippocrates used to measure the balance of blood, phlegm, yellow bile, and black bile to select the best therapy for every patient. Today we prefer to look at genetics rather than to these four fanciful humors, but the point is the same: the “one-fit-for-all” model is, at least for some available drug, not even desirable, as considerable advantages in treatment outcome (and side-effects avoidance) can be achieved through a personalized approach.

Just to explain the concept with a few numbers: only 30-60 % of patients respond properly to beta-blockers, anti-depressants, statins and antipsycothic agents. Adverse drug reactions can cause significant prolongation of hospital visits and, only in the USA, they are estimated to cause approximately 100,000 deaths every year. To learn more about pharmacogenomics:

CLINICAL VALIDITY OF PHARMACOGENOMIC TESTING

GENETIC VARIANTS OF PHARMACOGENOMIC RELEVANCE
PHARMACOGENOMICS AND  POPULATION DIVERSITY

PHARMACOGENOMIC DATABASES
COMMERCIAL KITS FOR PHARMACOGENOMIC TESTING