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Pharmacogenetics in the Cancer Clinic: From Candidate Gene Studies to Next-Generation Sequencing H-J Guchelaar1, H Gelderblom2, T van der Straaten1, JHM Schellens3,4 and JJ Swen1 Genetics has significantly added to our understanding of variability in drug response, especially in cancer treatment. Pharmacogenetics, aimed at predicting a patient’s chance for effective and safe drug treatment by interrogating germ line genetic variants, has moved from investigating a monogenetic candidate gene to examining complex phenotype-based genome-wide approaches. With the rapid advances in sequencing technologies, decline in costs, and swift turnaround times, large-scale genomic information will become available in the clinical setting, facilitating implementation of pharmacogenetics. Two of the oldest, and probably most fundamental, questions in clinical pharmacology are why drugs have a beneficial effect in only a proportion of patients and why they are harmful to some patients but not to others. These questions may seem contradictory because drugs are approved by regulatory authorities for use in humans only after having been proven efficacious and safe. Indeed, both efficacy and safety of drugs are studied and evaluated in patient populations, and it is accepted and inevitable that the outcome is not beneficial for every subject included in the study or for every patient in clinical practice. Spears et al.1 have analyzed the efficacy of major drug classes in several important diseases and showed that inherent variability in response rates is high. The highest percentage of patients responding was 80%, for cyclooxygenase 2 inhibitors, and the lowest was 25%, for cancer chemotherapy. Spear’s analysis was conducted in 2001, before the era of targeted drugs in cancer treatment. Overall, the targeted anticancer drugs have improved response rates as compared with the 25% of conventional chemotherapy, but they are still not effective in all patients for whom the drug is indicated. Moreover, with regard to drug safety, there is much to win. In the Harvard Medical Practice Study, the nature of adverse events in hospitalized patients was studied, and it was found that 3.7% of
the disabling injuries were caused by medical treatment and 19% represented adverse drug events. In the Dutch Hospital Admissions Related to Medication (HARM) study, which investigated the incidence of hospital admissions due to drugrelated problems, it was shown that 5.6% of acute hospital admissions were drug related. Chemotherapeutic drugs are toxic and lead to side effects by their nature; in addition, the newer targeted anticancer drugs, designed to specifically target the tumor and thus increase the tolerability, induce (severe) side effects at a high incidence. Pharmacogenetics is thought to be helpful here. It is considered one of the first clinical applications of the postgenomic era and promises personalized medicine rather than the established “one size fits all” approach to drugs and dosages. The aim of pharmacogenetics is, using a germ line DNA biomarker test, to predict which patients are more likely (not) to respond to a certain drug. The test may also indicate those patients who are more likely to experience side effects of drug treatment. The therapeutic recommendation of such a test result could include the choice of an alternative drug or adjustment of the drug dose. Oncology has been one of the very first medical specialties to take up and implement pharmacogenetics in clinical practice. One of the reasons may be that drug treatment in oncology harbors all characteristics that make the use of DNA biomarkers especially useful. Indeed, in oncology, the drug’s effect presents only after weeks to months (or years in the adjuvant setting), treatment delay has important clinical consequences, the prognosis is mostly poor, and treatment has a narrow therapeutic window with potentially severe side effects. A classic example of pharmacogenetics in oncology is the dose individualization of 6-mercaptopurine in childhood chronic myelogenous leukemia based on TPMT genetic polymorphism. Patients heterozygous for TPMT genetic variants *2, *3A, *3B, or *3C receive a 50% reduced dose, and patients homozygous for these decreased-activity alleles receive a 20-fold reduction of
1Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands; 2Department of Medical Oncology, Leiden University Medical Center, Leiden, The Netherlands; 3Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands; 4Department of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands. Correspondence: H-J Guchelaar (
[email protected])
Received 7 January 2014; accepted 9 January 2014; advance online publication 19 February 2014. doi:10.1038/clpt.2014.13 Clinical pharmacology & Therapeutics
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Prac tice the 6-mercaptopurine dose as compared with TPMT wild-type patients. Similarly, for DPYD genetic polymorphisms, a clearcut therapeutic dose recommendation is available for the treatment of cancer patients with 5-fluorouracil or capecitabine.2,3 Both TPMT and DPYD are red herrings in pharmacogenetics: although drug response in general is a complex trait, the enzyme activity of TPMT and DPD is largely under monogenetic control. Consequently, TPMT and DPD enzyme activities, as intermediary end points, can be predicted adequately from information on the genotype of a single gene. In addition, because the clinical toxicities of 6-mercaptopurine and 5-fluorouracil/capecitabine are strongly associated with TPMT and DPD enzyme activity, respectively, the decreased-activity genetic polymorphisms in the genes encoding these enzymes are good predictors of clinical drug toxicity. The relative rareness of monogenetic relationships in pharmacogenetics has made the candidate gene approach in pharmacogenetic research less popular. Indeed, the chance of finding a single variant gene with considerable explaining performance for a clinically relevant phenotype is low. Assuming that much of the variation in drug response is introduced by interpatient variability in pharmacokinetics and pharmacodynamics, it seems rational to apply a pathway approach to the discovery of pharmacogenetic biomarkers. In such an approach, the major metabolic pathways and drug transporters, together with drug targets and signaling pathways, are taken into account and explored for an association with variation in drug response. This method undoubtedly leads to a higher chance for success but also to a high number of candidate genes and alleles, requiring a large sample size in a pharmacogenetic association study. However, the genetic variants can be selected in a smart way using a tagging single-nucleotide-polymorphism approach, resulting in a relatively limited number of variants per gene but a high coverage of the total variation within that particular gene.
An obvious limitation of the above-mentioned approaches is that by design, they are limited by the knowledge of the pharmacokinetics and pharmacodynamics of the drug. It is not uncommon that, unexpectedly, transporters mediate a drug’s distribution or drugs have undiscovered mechanisms of action. However, platforms harboring a comprehensive set of genes and variant alleles related to drug metabolism and drug distribution, such as the drug metabolism enzymes and transporters or VeraCode ADME Core Panel arrays, are available and can be used to explore the role of variation in pharmacokinetics. The genome-wide association (GWA) approach in biomarker discovery is free of an a priori hypothesis and therefore not limited by the above-mentioned disadvantage of the candidate gene and pathway approaches (Figure 1). GWA studies have been used to identify common DNA sequence variants that are associated with susceptibility to many complex disease traits, such as diabetes, cancer, Alzheimer’s disease, Crohn’s disease, multiple sclerosis, and rheumatoid arthritis. However, with few exceptions, the odds ratios for the effects are typically in the range of 1.05–1.15, leaving the explanations for heritability of the traits largely unresolved.4 GWA studies have revolutionized pharmacogenetics research, especially regarding the discovery of predictive biomarkers for drug-induced toxicity. Indeed, recent years have shown numerous examples of the successful application of the GWA approach to identify genetic markers for drug-induced liver toxicity, drug hypersensitivity reactions, skin rash, and myelotoxicity. When considering all GWA studies, drug response–related GWA studies are sevenfold more likely to achieve odds ratios >3 as compared with common disease–related GWA studies.5 This is especially true for GWA studies aiming to identify predictive markers for adverse drug reactions, for which the odds ratios are typically in the range of 5–80. As a result, drug toxicity biomarker discovery studies require only a relatively small sample size of well-defined cases and controls (patients with and without the
Drug
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Transporter A
Drug
Enzyme X
Drug (intracellular)
Enzyme B
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Enzyme D
Inactive metabolite
Inactive metabolite Receptor C
Gene encoding X
Genes encoding A,B,C,D.....
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G A G AA C A GGTC A G CCA C C A C TAT G CC A GGT TC.........
~1,000,000 SNPs across the genome
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GWAS
Figure 1 In pharmacogenetics, methods have moved from candidate gene and pathway approaches using current knowledge of the drug’s pharmacokinetics and pharmacodynamics to hypothesis-free approaches, including GWAS and NGS. GWAS, genome-wide association studies; NGS, next-generation sequencing. 2
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Prac tice adverse effect), although these may be difficult to find in case of low-incidence side effects. For drug efficacy end points such as progression-free survival or overall survival, typically used in oncology, smaller odds ratios are most likely, necessitating large (hundreds to thousands) cohorts of patients to gain enough power. In fact, these GWA studies can be performed only through large international collaborative efforts and with extensive biobanking. Rare sequence variants may be especially relevant for finding genetic biomarkers predictive of adverse drug effects such as idiosyncratic toxicity. Due to the rapid advances in sequencing technologies, paralleled by a sharp decline in costs and shortened turnaround time, sequencing applications have become affordable and approachable in both research and clinical settings. This is especially true for oncology, in which exome sequencing is increasingly being used for identifying somatic mutations to guide treatment with targeted therapies. In contrast to GWA methods, these massively high-throughput sequencing efforts also provide data on most germ line variants of interest for drug response. Therefore, next-generation sequencing may not only lead to discovery of novel sequence variants involved in drug response but also allow the
Clinical pharmacology & Therapeutics
pharmacogenetic evaluation of genetic data and thus aid the implementation of the 250 well-established pharmacogenes in the clinical setting. In conclusion, with the rapidly advancing sequencing technologies, these techniques will not only become applicable in the clinical oncological setting to guide targeted therapy but also make germ line variants routinely available, which certainly will aid the implementation of pharmacogenetics in clinical patient care. CONFLICT OF INTEREST The authors declared no conflict of interest. © 2014 American Society for Clinical Pharmacology and Therapeutics
1. Spear, B.B., Heath-Chiozzi, M. & Huff, J. Clinical application of pharmacogenetics. Trends Mol. Med. 7, 201–204 (2001). 2. Swen, J.J. et al. Pharmacogenetics: from bench to byte–an update of guidelines. Clin. Pharmacol. Ther. 89, 662–673 (2011). 3. Caudle, K.E. et al. Clinical Pharmacogenetics Implementation Consortium guidelines for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine dosing. Clin. Pharmacol. Ther. 94, 640–645 (2013). 4. Harper, A.R. & Topol, E.J. Pharmacogenomics in clinical practice and drug development. Nat. Biotechnol. 30, 1117–1124 (2012). 5. Giacomini, K.M., Yee, S.W., Ratain, M.J., Weinshilboum, R.M., Kamatani, N. & Nakamura, Y. Pharmacogenomics and patient care: one size does not fit all. Sci. Transl. Med. 4, 153ps18 (2012).
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