Often new variants of a gene known to be associated with a trait (e.g. drug response) are identified. An important task is then to determine what impact the new variant may have on the same trait. The effects of many drugs are affected by genetic variants in metabolic enzymes because of altered dose-blood concentration relationships. CYP2C19, one of the cytochrome P450 metabolic enzymes, is highly polymorphic (many variants known in the population) and is known to alter the effect of many drugs in current use. Most variants are associated with complete or partial loss-of-function. A few years ago, an unusual new variant, CYP2C19*17, associated with increased metabolic activity was reported. We illustrate the assessment of the functional consequences of a new variant allele using CYP2C19*17 as a case-example.The original publication identifying CYP2C19*17 as a new variant allele can be found at: PubMed Our assessment can be found at: Wiley.com

Most drugs are effective in only a proportion of patients to which it is given. Pharmacogenetically-guided therapy promises to improve on selection of patients more likely to respond. In this case-example, we describe the development of gefitinib, a drug used for the management of non-small-cell lung cancer (NSCLC). When the drug was used in a broad population of NSCLC patients in a Phase III trial, the drug was not effective and ran the risk of being withdrawn from the market. Further work showed that the drug was effective if only patients showing specific genetic abnormalities in the epidermal growth factor receptor (EGFR) gene were chosen. The EGFR mutation leads to constitutive (always on) cell-growth signalling.The original publication on failure to show efficacy in a broad population ¹ can be found at The Lancet and evidence to show that better targeting can be achieved by genetic testing ² can be found at the Annals.Our assessment ³ can be found at the Annals.

References

1. Thatcher N, Chang A, Parikh P, Rodrigues Pereira J, Ciuleanu T, von Pawel J, et al. Gefitinib plus best supportive care in previously treated patients with refractory advanced non-small-cell lung cancer: results from a randomised, placebo-controlled, multicentre study (Iressa Survival Evaluation in Lung Cancer). Lancet 2005;366(9496):1527-37.
2. Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 2009;361(10):947-57.
3. Li-Wan-Po A, Farndon PA, Kobayashi S, Mitsudomi T, Potter VA. The Pharmacogenetic Rescue of Side-Lined Anticancer Drugs to the Front-Line: Gefitinib as a Case Example (February). Ann Pharmacother 2011.

One of the principal aims of pharmacogenetics is the identification of molecular abnormalities (biomarkers) underlying specific diseases. Once these abnormalities are identified, researchers can then focus on developing drugs that target them. An impressive success using this approach is the development of imatinib, a drug that has revolutionised the treatment of one two types of leukaemia, chronic myelogenous leukaemia (CML) and gastrointestinal stromal tumours (GIST). Its development followed the identification of a genetic abnormality that leads to the production of a CML-inducing protein (an oncoprotein). Imatinib is a drug that inhibits this protein and therefore stops CML.A common problem in clinical practice is that different patients often need different doses of the same drug to achieve the same blood concentration and imatinib is no exception.This case-example highlights two aspects in the clinical use of imatinib: (i) Pharmacogenetics – the selection of patients on the basis of the underlying molecular abnormality (the oncoprotein BCR-ABL associated with an abnormal chromosome called the Philadelphia chromosome) and, (ii) therapeutic drug monitoring - the measurement of the blood concentration of the drug to ensure that it is above the minimum effective concentration (MEC). More specifically, this case-example considers how one might go about defining the MEC mathematically.The original report of the study demonstrating the dramatic efficacy of imatinib in CML ¹ can be accessed at the National Journal of Medicine and a summary of our case-example describing how the MEC can be determined can be accessed at Springer.

References

1. Druker BJ, Talpaz M, Resta DJ, Peng B, Buchdunger E, Ford JM, et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N Engl J Med 2001;344(14):1031-7.

When we discuss personal genomics, the genome we focus on is usually our nuclear genome. We also consider our mitochondrial genome, albeit less frequently. More recently, increasing attention has been drawn to the potential impact of our microbiome, the assembly of microbes inhabiting our body even in health, in relation to both disease susceptibility and response to drugs. For example, there is increasing evidence that our microbiome interacts closely with our immune system to alter our susceptibility to immunological disease such as diabetes and Crohn’s disease. Moreover, there is also accumulating evidence of a link between our microbiome and metabolic diseases, notably obesity. In this case-example we comment on the potential impact of our microbiome on the adverse effects of drug therapy.The original publication we focus on can be found at: PubMed Our assessment can be found at: Wiley.

A gene-expression signature can be defined as a set of DNA-related molecular markers, usually quantified by the use of microarray technology, with presumed predictive value in classifying disease or patient according to prognosis or likelihood of drug response. Commercially available gene signatures include Mammaprint®, a 70-gene signature for classifying early breast cancer patients according to likelihood of developing distant metastases ¹ and Oncotype DX®, a 21-gene signature for predicting breast cancer recurrence is patients receiving tamoxifen ² . The claimed potential advantage of gene signatures is that such classifications would be more accurate than with the use of clinicopathological classifiers based on patient and pathological tissue characteristics ³. However, careful validation is required to ensure that any genomic signature performs well enough in clinical practice. Some signatures have been found to be unreliable and claims and publications based on them have had to be withdrawn ^4-6.In our case-example, we investigated the extent to which a published gene signature was useful for identifying those at high risk of prostate cancer. Our view was that while the study was well conducted, there was still considerable work required for the clinical validation of the gene signature.The original publication can be found at: the National Journal of Medicine Our assessment can be found at: Karger.

References

1. Mook S, Van't Veer LJ, Rutgers EJ, Piccart-Gebhart MJ, Cardoso F. Individualization of therapy using Mammaprint: from development to the MINDACT Trial. Cancer Genomics Proteomics 2007;4(3):147-55.
2. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351(27):2817-26.
3. Acharya CR, Hsu DS, Anders CK. Gene expression signatures, clinicppathological features, and individualized therapy in breast cance. Jama 2008;299:1574-94.
4. Potti A, Dressman HK, Bild A, Riedel RF, Chan G, Sayer R, et al. Genomic signatures to guide the use of chemotherapeutics. Nat Med 2006;12(11):1294-300.
5. Acharya CR, Hsu DS, Anders CK, Anguiano A, Salter KH, Walters KS, et al. Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer. Jama 2008;299(13):1574-87.
6. Acharya CR, Hsu DS, Anders CK, Anguiano A, Salter KH, Walters KS, et al. Retraction: Acharya CR, et al. Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer. JAMA. 2008;299(13):1574-1587. Jama 2012.

Marfan syndrome is characterised by aortic aneurysm but shows considerable heterogeneity in clinical presentation. The diagnostic criteria for the syndrome (Ghent nosology), are outlined by international expert opinion (1). Identification of the major genetic abnormality underlying the aortic complication has quickly led to the evaluation of potential therapies for preventing and retarding the progression of the aortic complications. In this case-example we illustrate the rapid pace of progress along the translational path from identification of the genetic contribution to disease and drug therapy, particularly when these relate to drugs with long histories of use in other conditions.The original publication can be found at: http://jmg.bmj.com/content/47/7/476.long

Our assessment (2) can be found at: http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2125.2011.03929.x/abstract;jsessionid=B97F614E84B9E274628326AE59ACBE66.d02t01

References 

1. Loeys BL, Dietz HC, Braverman AC, Callewaert BL, De Backer J, Devereux RB, et al. The revised Ghent nosology for the Marfan syndrome. J Med Genet 2010;47(7):476-85.
2. Li-Wan-Po A, Loeys B, Farndon P, Latham D, Bradley C. Preventing the aortic complications of Marfan syndrome: a case-example of translational genomic medicine. Br J Clin Pharmacol 2011;72:6-17.