Interview: Perspective on the Use of Genomic Biomarkers in the Clinical Setting

Abstract

José AG Agúndez speaks to Emma Sinclair, Assistant Commissioning Editor

José AG Agúndez is a Doctor of Medicine and Professor in the Department of Pharmacology at the University of Extremadura (Extremadura, Spain) He is involved in pharmacogenomics research and he has taught pharmacology and pharmacogenomics to medical and veterinary students since 1986. Presently he coordinates the genomics node of the Spanish Network of Adverse Reactions to Allergens and Drugs (RIRAAF) and he serves as a consultant for several international research organizations.

▪ After completing your medical degree at the University of Extremadura, how did you become interested in the field of pharmacogenomics?

My main professional interest always was clinical practice, but even as an undergraduate student I found myself very interested in the molecular insights of human disease and response to therapy. After completing my medical degree I was accepted as a PhD student in the Department of Biochemistry & Molecular Biology at the University of Extremadura (Extremadura, Spain). My PhD Thesis, directed by Professors Carlos Cameselle and Antonio Sillero, focused on enzymology. Hence, when I moved to the Department of Pharmacology to work with Professor Julio Benítez as a post-doc, the logical focus of my work was on enzymes involved in drug metabolism. By the late 1980s and early 1990s I moved to the Department of Pharmacology at Biozentrum der Universität Basel (Basel, Switzerland), where I worked under the expert guidance of Professor Urs A Meyer. During this post-doc period, molecular pharmacogenomics was evolving rapidly and the Meyer laboratory was the epicenter of many crucial discoveries on genetic variations related to drug-metabolizing enzymes. There, I was seized by the power of molecular pharmacogenomics to predict enzyme behavior and thereby drug pharmacokinetics. When I was appointed as a Professor at the Medical School at the University of Extremadura, I started working combining my interests in medicine, enzymology, pharmacology and pharmacogenomics.

▪ How has the field of pharmacogenomics most notably changed since you first started out as a researcher?

The most obvious changes are the technical ones. When I started as a researcher we used to spend several days and a considerable amount of resources on obtaining a short DNA sequence. Today most laboratories working in pharmacogenomics can obtain much more sequence information for little money and within the day. Another change, perhaps more subtle, is how our view of pharmacogenomics evolved. Initially we had a rather naive view of the field, assuming that the main potential of pharmacogenomics would lie in the hypothesis that alterations in genes coding for drug-metabolizing enzymes would cause a change in the metabolic capacity resulting in accumulation of the drug, and that this accumulation would then change the drug response. This is true in some cases but not as many cases as we initially expected. Reality shows that pharmacogenomics alone is not sufficient to explain all the interindividual variability in drug response, and in recent years many articles have been published indicating lack of association between polymorphisms in genes coding for drug-metabolizing enzymes and drug response, even though these drugs are substrates for the enzymes encoded by the target genes.

▪ What do you think have been the most important findings to date?

In my opinion two genes played a key role in the development of pharmacogenomics. These are CYP2D6 and NAT2. Both genes share an important feature that facilitated the study of the functional implications of their genetic variations: these genes are virtually not inducible and therefore loss-of-function genetic variations usually cause a neat and reproducible effect in vivo. In addition CYP2D6 is one of the best examples in pharmacogenomics for functional implications of gene duplication and amplification. NAT2 is one of the best examples in pharmacogenomics for the need of haplotype reconstruction for phenotype inference, thus pointing the way to the combined use of genetic and bioinformatics in phenotype prediction. Moreover, for both genes, algorithms considering the differential effect of particular loss-of-function variant alleles in phenotype inference have been developed. Much of pharmacogenomics research is inspired in the findings obtained with CYP2D6 and NAT2. What is somewhat striking is that the most important pharmacogenomics findings to date were obtained by using relatively low-powered technologies, such as traditional sequencing or PCR-based techniques, and that the advent of genome-wide association studies or exome-sequencing technologies has not yet led to the expected revolution in pharmacogenomics that was predicted. However promising findings in drug-induced liver injury have been obtained with these high-powered technologies and many studies are still underway. It is likely that some apparent genotype/phenotype discrepancies and some uncertainties in phenotype inference will be solved using these high-throughput techniques.

▪ You have a particular interest in the clinical use of genomic biomarkers. What would you say are the most important potential applications of genomic biomarkers in the clinical setting?

We could think of genetic biomarkers as indicators of pathogenic processes or indicators of response to therapeutic interventions. These genetic biomarkers can be used in the clinical setting for disease diagnosis, prognosis, therapy selection, dose adjustment and monitoring outcomes. While the most conservative pharmacogenomics views aim to stratify patient populations into those who should or should not receive a given drug, several guidelines are intended to adjust drug dose based on pharmacogenomics tests. The Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Pharmacogenomics Knowledge Base [101], as well as other international initiatives, are particularly active in providing guidelines and therapeutic recommendations based on pharmacogenomic testing.

We are prepared to recommend pharmacogenomics-based dose adjustment for a limited number of well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings. To date CPIC has published nine gene–drug guidelines and several new guidelines are at various stages of completion.

▪ What obstacles do clinicians face when using genomic biomarkers? How could these be addressed?

When we analyzed these obstacles in Spain, the highest ranked obstacle was the lack of institutional support, followed by the need for clear guidelines for the use of genomic markers in clinical practice, insufficient awareness of the potential of these biomarkers among clinicians, and the need for demonstration of clinical validity and utility of genomic biomarkers. These barriers seem to be common in most countries that carried out surveys regarding this issue. In my opinion all these barriers are linked: once guidelines and protocols for the clinical use of genomic biomarkers are ready, the major groups of mentioned barriers would weaken and, in consequence, this will facilitate institutional support and promotion of the use of genomic biomarkers.

That is why I think that the most urgent issue is the development of guidelines and protocols. Ideally these should be consensus guides based on international initiatives and elaborated on according to a detailed and reproducible protocol. For instance for CPIC, one of the most active consortia in this field, membership now spans 12 countries and includes over 100 members from 58 institutions and multiple observers from the NIH and the US FDA. In the interest of transparency and reproducibility we will shortly publish the development process of the CPIC guidelines.

▪ How could genomic and phenomic biomarkers be used together? What are the advantages of this?

We know that interindividual variability in the association of particular genotypes and phenotypes exists. Individuals with identical sequences for a determined gene often show major differences in the expression, in the activity or in the clinical consequences of the gene products. For instance, we know that in some cases, factors such as gender, age, dietary or lifestyle habits and many others influence genotype–phenotype or genotype–pathogenicity relationships. Presystemic metabolism constitutes a major source of uncertainty in drug pharmacokinetics that may influence the predictive capacity of pharmacogenomics tests. For instance, the gastrointestinal flora has a collective metabolic activity equivalent to a virtual organ and it is subject to major changes according to dietary and lifestyle factors that are not yet well understood.

When genomics started to evolve some decades ago we always thought of genetic–environmental interactions as a relevant cause of many complex diseases or altered drug response. However the scientific community puts most effort into genetics and insufficient attention has been paid to the rest of the factors. Today we are way ahead in our genotyping capabilities than in the phenotyping characterization of patients. The first studies combining the exploration of phenotypic structure and genotypic variations, known as phenome-wide association studies, started to be published only recently. Typically these studies analyze several hundreds of phenotype characteristics, analytical data and data related to the clinical presentation and evolution of the disease, combined with high-throughput genotyping techniques. Ideally, associations obtained with genomic biomarkers should be refined with the use of phenomic biomarkers. The analysis of all available information of patients will definitively confirm or discard suspected associations and hopefully will identify relevant combinations of phenomic and genomic biomarkers that could be used in the clinical setting.

▪ Which areas involving genomic biomarkers do you think need to be addressed most urgently in future research?

The use of genomic biomarkers involves several areas that are interdependent and that include identification, validation, refinement, development of clinical guidelines and then clinical use. The use of high-throughput genotyping techniques will greatly help in the identification and validation processes. For these processes it is particularly important to use next-generation sequencing of complete genes instead of SNP analyses, to identify as yet unknown relevant gene variations. Conventional pharmacogenomic genotyping classifies alleles as functional by exclusion, that is, when no common enzyme inactivating mutations are identified. However a plethora of loss-of-function and impaired-function alleles exist for many genes relevant in genomics testing, and therefore unless patients are genotyped by using whole-gene sequencing, the chances of mistyping are relatively high. Whole-exome sequencing holds great promise for the identification of relevant combinations of pharmacogenomic biomarkers for drugs that follow several metabolic pathways and to assess the influence of other gene variations in the expression of a particular gene. Refinement requires the combination of phenomics and genomic data. Although we are just starting to understand how the combination of these factors influences the risk of developing complex diseases, clinical outcomes and response to therapy, we already have developed algorithms combining demographic, virologic, biochemical and genetic traits for predicting the response to antiviral drugs, and many studies are underway. Presently, within the frame of the Spanish Health Ministry network RIRAAF, a research structure for the investigation of adverse reactions to drugs and allergens, we are developing a huge database including phenotyping and genotyping information to conduct association studies. Finally, development and continuous updating of clinical practice guidelines is a crucial process that should be implemented.

▪ How do you see the clinical use of genomic biomarkers progressing within the next 10 years?

I am fairly optimistic. Issues related to biomarker identification, validation and refinement will greatly improve in the next few years with the increased use of high-throughput genotyping techniques and phenome-wide association studies. Some guidelines for the clinical use of these genomic biomarkers and some biomarker-based prediction algorithms are already available and their numbers keep increasing at a rapid pace. Although updates for these algorithms and guidelines will certainly be necessary to incorporate new information and to refine the predictive capacity of genomic biomarkers, the mere use of these algorithms and guidelines and the evaluation of their performance in the clinical setting would provide priceless feedback that we can use to improve all processes involved in the clinical use of genomic biomarkers.

Disclaimer

The views expressed in this article are those of the interviewee and do not necessarily reflect the views of Future Medicine Ltd.

Financial & competing interests disclosure

JAG Agúndez has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.