Review

Asthma genetics and personalised medicine Deborah A Meyers, Eugene R Bleecker, John W Holloway, Stephen T Holgate

Unbiased genetic approaches, especially genome-wide association studies, have identified novel genetic targets in the pathogenesis of asthma, but so far these targets account for only a small proportion of the heritability of asthma. Recognition of the importance of disease heterogeneity, the need for improved disease phenotyping, and the fact that genes involved in the inception of asthma are likely to be different from those involved in severity widens the scope of asthma genetics. The identification of genes implicated in several causal pathways suggests that genetic scores could be used to capture the effect of genetic variations on individuals. Gene–environment interaction adds another layer of complexity, which is being successfully explored by epigenetic approaches. Pharmacogenetics is one example of how gene–environment interactions are already being taken into account in the identification of drug responders and nonresponders, and patients most susceptible to adverse effects. Such applications represent one component of personalised medicine, an approach that places the individual at the centre of health care.

Introduction Genetics and genomics research, including studies into asthma susceptibility, severity, and response to treatment (pharmacogenetics), are key components of stratified medicine (also referred to as personalised or precision medicine; panel 1). The use of a multigenetic approach (based on an additive genetic model or a more complex model that incorporates both genetics and genomics) to develop genetic profiles or genetic scores for disease susceptibility, integrated with clinical subphenotypes, will lead to early disease prediction, allowing for targeted interventions.12,13 Molecular profiling will be used to identify at-risk individuals to develop strategies that have the potential to affect the development and natural history of complex disorders such as asthma throughout the patient’s life. The genetic profiles that determine disease heterogeneity, progression, and severity are different from those that confer initial disease susceptibility (ie, they involve genes from different pathways). Defining gene variants that affect disease severity will delineate genetic profiles that better define individuals at greatest risk of severe or progressive disease. Genetic severity profiles represent an important step forward in the development of new therapeutic targets based on the identification and understanding of fundamental pathobiological mechanisms. A pathway-directed approach to asthma will allow targeted drug discovery, especially for new biological drugs intended to modify disease progression and prevent the development of severe disease. Asthma susceptibility and severity result from the interaction of genetics and genomics profiles with environmental exposures at different times during the life course (figure 1). Genes associated with susceptibility interact with environmental risk factors, causing early mild or intermittent asthma. Different genes then interact with additional environmental exposures or epigenetic factors, leading to disease progression. The varied clinical patterns seen in severe asthma reflect individual genetic profiles combined with environmental exposures that initiate persistent bronchial inflammation and tissue injury, leading to pathophysiological abnormalities and www.thelancet.com/respiratory Vol 2 May 2014

airway wall remodelling.14 One of the characteristics of severe asthma is resistance or reduced responsiveness to treatment.15,16 Pharmacogenetic interactions might affect therapeutic responses, reducing sensitivity to specific treatments and leading to disease phenotypes of increased severity. Thus, characterisation of asthma severity phenotypes, including predictive genetic and genomic biomarkers, will lead to an improved understanding of disease heterogeneity and progression, contributing to the development of new targeted treatments.

Genome-wide association studies

Lancet Respir Med 2014; 2: 405–15 Published Online May 2, 2014 http://dx.doi.org/10.1016/ S2213-2600(14)70012-8 Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA (Prof D A Meyers PhD, Prof E R Bleecker MD, Prof J W Holloway PhD); and Human Development and Health, and Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton, UK (Prof S T Holgate DSc) Correspondence to: Prof Stephen T Holgate, Clinical and Experimental Sciences, Southampton General Hospital, Southampton SO16 6YD, UK [email protected]

Over the past decade, genome-wide association studies (GWAS) have been used extensively to investigate the genetic bases of common complex diseases, including asthma.17,18 Before GWAS, many candidate gene studies were done for asthma susceptibility;19,20 however, most of the positive associations were not replicated in GWAS, because of differences in either phenotype definition or the populations studied (either in terms of ancestry or environmental exposures), or because of false positives and small sample sizes in the candidate gene studies.20 GWAS use genotyping arrays with up to millions of single-nucleotide polymorphism (SNP) markers located throughout the genome. This method provides an unbiased or hypothesis-independent means to identify the underlying genetic variants that contribute to disease, its

Key messages • In addition to environmental exposures, genetic factors have an important effect on the inception, severity, and treatment of asthma. • Genome-wide association studies have been used to identify many genes associated with asthma, the functions of which are incompletely known. Polymorphisms of different genes affect the origins of asthma, its severity, and responses to treatment. • Investigation of epigenetic processes (such as CpG methylation and histone modification) and genome-wide interaction studies are providing new insights into how environmental and genetic factors interact. • Genetic profiles or scores are being used to assess asthma susceptibility, disease natural history, and therapeutic responses in individuals—an example of personalised or stratified medicine.

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Panel 1: Timeline for genetic discoveries leading to personalised medicine • 1997: US National Institutes of Health consensus report on cystic fibrosis genetic testing recommends that couples planning a pregnancy should be tested for carrier status.1 • 2001: International Human Genome Sequencing Consortium reports the first analysis of the human genome, suggesting that it contains only 30 000–40 000 genes (not 100 000 as previously believed).2 • 2004: First genotype-stratified clinical trial for asthma3 shows decreased responsiveness to shortacting β2-adrenoceptor agonists for Arg16/Arg16 ADBR2 homozygotes. • 2004: International Human Genome Sequencing Consortium reports finished analysis of the complete human genome, showing that it contains only 20 000–25 000 genes.4 • 2005: International HapMap Consortium produce a catalogue of human genetic variation, accelerating the search for genes in common diseases such as asthma.5 • 2007: First genome-wide association study (GWAS) for asthma susceptibility reported.6 • 2008: Announcement of the 1000 Genomes Project to sequence the genomes of at least 1000 people around the world. • 2010: European GABRIEL Consortium reports results from a GWAS for asthma susceptibility,7 identifying several risk genes. • 2011: US EVE Consortium reports results from a GWAS8 showing difference in asthma susceptibility risk genes in different ethnic groups and races. • 2011: US National Human Genome Research Institute publishes Charting a course for genomic medicine from base pairs to bedside,9 articulating a vision for the future of genomics research and charting the path towards an era of genomic medicine. • 2011: First GWAS for response to corticosteroid treatment in asthma reported.10 • 2012: First GWAS for lung function in asthma reported.11 • 2013: A decade after the start of the Human Genome Project: • Cost of sequencing a human genome reduced from about US$1 billion to about $3000. • Time to sequence a whole genome reduced from 6–8 years to 1–2 days. • More than 1542 GWAS reported. • Number of genes with known disease-phenotype-causing mutations increased from 53 to 2972. • Drugs with pharmacogenomics information on the label increase from four to 106. • 2013: FDA authorises several next-generation sequencing devices for use in clinical medicine.

Genetics • Susceptibility-associated phenotypes such as atopy and bronchial hyper-responsiveness • Disease expression and progression such as severity and pharmacogenetics

Early intermittent asthma

Early-life environment factors and epigenetics • Including prenatal influences (prematurity), allergens, respiratory infections, tobacco smoke, air pollutants, diet, lung development, etc

Chronic persistent progressive disease • Continued inflammation • Mucous hypersecretion • Airway wall remodelling

Figure 1: Genetics in the development and progression of asthma A combination of genetics and environmental exposures leads to early intermittent asthma. Additional gene variants and further environmental exposures lead to chronic persistent disease with heterogeneous subtypes.

severity, and partial phenotypes such as lung function and bronchial hyper-responsiveness. The first comprehensive GWAS in asthma was reported by Moffatt and colleagues6 in 2007, and identified a novel locus on 17q21 containing several genes 406

including ORMDL3 and GSDMB. This study revealed the potential for GWAS to be used to uncover novel susceptibility genes and thus to identify previously unknown biological processes involved in asthma susceptibility. 17q21 has been the most consistently identified locus associated with asthma in subsequent GWAS.7,8,21–24 GWAS that used genotyping arrays, which provide information about more variants, and larger case-control populations have identified additional loci consistently associated with asthma, including IL33 on chromosome 9p24,7,8,25 HLA-DR and HLA-DQ genes on 6p21,7,8,26–31 IL1RL1 and IL18R1 on 2q12,7,8,21,25 WDR36 and TSLP on 5q22,7,8,25,27,30 and IL13 on 5q317,26 (table). In populations with different racial backgrounds, GWAS have uncovered evidence for loci that could be ethnic specific—eg, PYHIN1 in African-Americans with asthma.8 However, as with other common diseases, individual genes identified in GWAS do not account for a large amount of the heritability of asthma; a multigenetic approach is therefore needed. To explore this approach, investigators of an Australian GWAS24 assessed the use of genetic loci identified by the European GABRIEL Consortium to predict disease status.7 Although a multiSNP score computed on the basis of the ten most associated loci reported in the GABRIEL study was significantly associated with asthma in the Australian study population (p=8·2 × 10−¹⁵), sensitivity and specificity were low, emphasising the need for even more complex genetic models and improved phenotyping for disease prediction. The low risk conferred by disease-associated variants reflects the complex interactions between different genetic and environmental factors that underlie complex diseases. In large GWAS, genetic variation is measured across a range of often unknown environmental exposures and the effects of gene–gene interactions (epistasis) are ignored. This issue has led some researchers to question the usefulness of genetic approaches for prediction of complex diseases.33 However, this interpretation ignores the potential for important insights into disease mechanisms through the identification of previously unrecognised biological pathways associated with disease pathogenesis. Use of a simple phenotype of asthma diagnosis (often doctor-diagnosed asthma because of the need to increase sample size by combining samples across cohorts) means that the effect of genetic variants is diluted because many different asthma phenotypes (observable characteristics or traits of the disease) or endotypes (distinct functional or pathobiological mechanisms) are analysed together.34 Genetic variants are often assumed to contribute equally to disease susceptibility. However, at the simplest level, although some loci are associated with both childhood and adult-onset asthma,30 some genomic regions are unique to each group.7,35 Studies have also shown the benefit of using an unbiased clustering approach in multidimensional data to identify different asthma www.thelancet.com/respiratory Vol 2 May 2014

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phenotypes.36,37 Definition of asthma endotypes with more precision will allow more accurate identification of genetic and environmental risk factors, preventing these signals from being diluted by phenotypic heterogeneity. For example, a GWAS analysis38 for wheeze phenotypes, identified through latent class analysis in a large longitudinal birth cohort, provided evidence of aetiological differences between wheezing syndromes. Although GWAS have been useful for many common diseases, some limitations have to be taken into account. First, most GWAS panels contain common but not rare variants and have been developed for populations of white European descent. Therefore, these panels might not be appropriate for other populations. Second, although most genes are well represented on genotyping panels, when investigating a specific gene, it is important to determine if the appropriate SNPs are on the panel. A negative result could be due to there being insufficient SNPs to completely cover the gene. Third, GWAS results are usually based on a single-gene model, not taking into account the effect of multiple genes or the environment. Fourth, although replication is important to avoid false positives due to multiple testing, a non-replicated finding does not always imply a false-positive result. The demographic characteristics and clinical features of the original and replicate populations need to be similar in replication studies so that phenotypic factors do not introduce additional variance into the analyses. Fifth, GWAS are often done in populations with a physician’s diagnosis with little other clinical data to achieve a large sample size. In such cases, further investigation of the clinical profiles of individuals with at-risk genotypes is not possible.

Gene–environment interactions GWAS approaches are being extended to gain further insights into the genetic basis of asthma.39 For example, use of genome-wide gene–environment interaction studies could provide important insights into the biological mechanisms that underlie responses to specific environmental exposures. Ege and colleagues40 did a genome-wide interaction study to investigate the effect of early life exposure to a farming environment on the risk of childhood asthma. Counterintuitively, they uncovered rare (but, notably, not common) variants, such as GRM1, that interacted with farming status to affect asthma development. In a study41 of two birth cohorts that made use of previously reported GWAS results, evidence emerged for a significant association between variants in the ORMDL3–GSDMB loci and development of asthma in children who had rhinovirus-induced wheezing in early life, but not in those for whom wheezing had been triggered by respiratory syncytial virus. Despite the challenges of statistical power and data collection, environmental interactions remain an important focus of investigation.42 www.thelancet.com/respiratory Vol 2 May 2014

Chromosome Population (first reported)

SNP (first reported)

ORMDL36–8,21–24

17

European

rs7216389

IL1RL17,8,21,25

2

European

rs1420101

WDR367,8,25

5

European

rs2416257

PDE4D32

5

European-American

rs1588265

DENND1B23

1

European-American, African-American

rs2786098

RAD507,26

5

European-American

rs2244012

IL137,26

5

European-American

rs20541

HLA-DR/DQ7,8,26–31 6

European-American

rs1063355

GSDMB6,7,8,21

17

European

rs2305480

IL337,8,25

9

European

rs1342326

IL18R17,21

2

European

rs2771166

SMAD37

15

European

rs744910

IL2RB7

22

European

rs2284033

RORA7

15

European

rs11071559

HLA-DPA127

6

Japanese, European-American, African-American

rs987870

TSLP8,25,27

5

Hispanic

rs1837253

PYHIN18

2

African-American

rs1101999

NOTCH430

6

Japanese

rs404860

AGER30

6

Japanese

rs204993

C6orf1030

6

Japanese

rs3129943

PBX230

6

Japanese

rs204993

IKZF430

12

Japanese

rs1701704

CDK230

12

Japanese

rs2069408

USP3830

4

Japanese

rs7686660

GAB130

4

Japanese

rs3805236

GATA330

10

Japanese

rs10508372

IL6R24

1

European

rs4129267

LRRC3224

11

European

rs7130588

C11orf3024

11

European

rs7130588

TNIP126

5

European-American

rs10036748

Only genome-wide significant genes or those identified in at least two genome-wide association studies (GWAS) are shown. SNP=single-nucleotide polymorphism.

Table: Summary of major genetic loci for asthma susceptibility identified by GWAS

Chemical modification of DNA and histone proteins that can be passed down to the offspring (epigenome) could have a crucial role in translating environmental interactions into changes in expression of specific diseaserelated genes. Epigenome-wide association studies are a promising approach through which this interaction can be systematically explored.43 However, investigators have to be cautious when the direction of causality is unknown, since epigenetic changes could be either a cause or a consequence of disease.44 Epigenetic studies for asthma are now underway and the usefulness of combining epigenetic with genetic data has been shown.45,46 For example, DNA methylation modulates the risk of asthma related to genetic variants in IL4R, suggesting that combining genetic and epigenetic assessments could lead to greatly improved disease prediction in the future.47 Increasing knowledge about the functionality of the genome will also improve interpretation of GWAS results. Integration of GWAS data with datasets such as those 407

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provided by the ENCyclopedia Of DNA Elements (ENCODE) project48 is providing insights into the mechanisms of action of disease-associated gene variants to elucidate new biological pathways linked to asthma. The ENCODE Consortium is an international collaboration funded by the US National Human Genome Research Institute to build a comprehensive list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and the circumstances in which genes are active. ENCODE will provide additional insight into pathogenic mechanisms by use of the many moderately associated SNPs identified in GWAS, which are often disregarded to reduce the potential for type I errors.49 Another useful approach is the comparison of genetic analyses across species; Himes and colleagues50 identified a new asthma candidate gene in human beings that encodes the Kv channel-interacting protein 4 (KCNIP4) by integrating a mouse bronchial hyper-responsiveness GWAS with human asthma and bronchial hyperresponsiveness GWAS data.

Genetics of lung function and asthma severity The major emphasis of genetic studies in common diseases such as asthma has been on disease susceptibility. However, increasing evidence shows that a proportion of genes and genetic variants important in asthma progression and severity differ from those that determine susceptibility.11 This is an important finding since genes related to severity might expose causal pathways that are more relevant targets for new treatments than those that confer susceptibility. Traditional definitions of severe asthma include patients who are uncontrolled despite maximum standard treatments.15,16 However, within this broad definition, subpopulations exist, each with different phenotypic characteristics.36,37,51 Several early GWAS compared patients with severe asthma with individuals without asthma.21,26 As might be expected, these GWAS all reported previously described asthma susceptibility genes. These patients were more likely to have a firm asthma diagnosis because of the severity of their disease than those assessed in other GWAS, which tended to rely on a physician’s diagnosis of asthma (for which misclassification is possible). Thus, when investigating genetics of disease severity and progression, patients with severe phenotypes need to be assessed and compared with controls with mild disease. An objective measure closely related to asthma severity is baseline lung function, specifically percentage predicted forced expiratory volume in 1 s (FEV1), which provides a measure of excess decline in lung function over time.37,51 Genetic studies of lung function in the general population should provide insight into genes that are important in determining lung function in patients with asthma.52 Large meta-analyses53,54 of GWAS for lung function in the general population (cross-sectional data) 408

done in participants of white European descent have provided evidence for several genes that affect lung function, such as HHIP, which encodes hedgehog interacting protein and is associated with differences in either FEV1 or the FEV1-to-forced vital capacity ratio. To determine if genetic variants in genes associated with lung function in the general population also modulate lung function in asthma, Li and colleagues55 tested 14 SNPs in the 11 genes previously identified in general populations for their possible association with pulmonary function, in patients with asthma. They noted significant associations for several genes, including HHIP, for which each copy of the risk allele was associated with a 252 mL reduced FEV1 in African-Americans and an 85 mL reduced FEV1 in people of white European descent. This finding has led to the development of a genetic score (ie, a measure of the cumulative effects of multiple genetic variants in different genes on a disease or trait) for asthma severity (figure 2), whereby an increased frequency of severe disease, based on either the American Thoracic Society severe asthma classification51 or clinical asthma severity clusters,37 was associated with an increased number of risk alleles in a five-gene model. A GWAS of lung function in asthma showed that genes in the T helper lymphocyte 1 (Th1) pathway affect disease severity, whereas genes in the pro-allergic Th2 pathway are important in disease susceptibility.11 Since genes associated with asthma susceptibility differ from those implicated in disease severity, it is important to define these severity gene pathways to improve our understanding of the pathophysiology of asthma progression and to find out why only a subset of patients go on to develop severe disease (figure 3). A multigene model is necessary to develop a genetic score that can be used to predict indicators of progressive disease, such as a decline in lung function over time. Additional studies are needed in asthma cohorts, with longitudinal follow-up that incorporates measures of asthma heterogeneity. For example, in the US National Institutes of Health Severe Asthma Research Program cluster analysis,57 patients with allergic asthma of increasing severity were identified (mild, moderate, and severe clusters with a high degree of family history of asthma) who seemed to differ genetically from patients with late-onset asthma and non-smoking asthmatics with fixed airflow obstruction (who had a lower frequency of positive family history of asthma).

Variation in drug responsiveness Pharmacogenetics The role of genetic variation in response to pharmacological treatment is another important focus of asthma genetics research. Pharmacogenetics is another example of gene–environment interaction, wherein the environment is exposure to a chemical or biological drug and the outcome is a therapeutic drug response (including adverse events). Several examples from different classes www.thelancet.com/respiratory Vol 2 May 2014

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80

ATS severity classification (p=0·0006) Asthma clusters (p=0·0027)

Th2 pathway genes

Other risk genes

Asthma risk

70

Proportion of participants with severe asthma (%)

Lung function genes

Th1 pathway genes

Disease progression

60

Figure 3: Genetics of asthma severity Asthma susceptibility is associated with genetic variants in the T helper lymphocyte 2 (Th2) pathway. Once asthma develops, disease progression is affected by variants of genes that are important in normal lung function, and genes involved in the Th1 and Th2 pathways, with some variants associated with decreased lung function and severe disease. Adapted from reference 56, by permission of Elsevier.

50

40

30

20

Pharmacogenetics

0

1

2 Number of risk SNPs

3

4–5

Figure 2: Association of severe asthma (based on ATS classification and asthma clusters) with increasing frequency of risk alleles in a five-gene model In this additive model, the proportion of participants with severe asthma is significantly positively associated with the number of risk alleles. SNPs=single-nucleotide polymorphisms. ATS=American Thoracic Society. Adapted from reference 55, by permission of Elsevier.

of drugs used for asthma management can be used to emphasise different key ideas in pharmacogenetic studies, and to show how these approaches have an integral role in personalised medicine. Unlike the discussion about asthma susceptibility, this section focuses on candidate gene pharmacogenetic studies, since only a few GWAS have been done that assess response to treatment. However, GWAS are appropriate for pharmacogenetics and should be done as part of future clinical trials.

β agonists Shortacting β2-adrenoceptor agonists (SABAs) and longacting β2-adrenoceptor agonists (LABAs) are the most widely prescribed drugs for the treatment of bronchoconstriction and long-term symptom relief in asthma. Pharmacogenetic studies have concentrated on coding variants in the β2-adrenergic receptor gene (ADRB2) to test for an association with short-term bronchodilator response (ie, bronchodilator responsiveness assessed in a clinical setting) and to identify a subgroup of patients whose symptoms get worse during this symptomatic treatment.58,59 www.thelancet.com/respiratory Vol 2 May 2014

One of the first genotype-stratified trials was the BARGE study3 by the Asthma Clinical Research Network, in which patients with asthma were prospectively stratified by Gly16Arg ADRB2 genotype. The rationale for a genotype-stratified trial was to ensure that sufficient numbers of patients with each genotype were included to have adequate statistical power. In this case, only the two homozygote genotypes were included (Arg/Arg and Gly/ Gly). Patients were randomly assigned to receive either regular dosing or intermittent dosing of a SABA, then were crossed over to receive the alternative treatment.3 The results showed that Arg16 homozygous patients improved when receiving placebo with intermittent SABA as needed, compared with no improvement on regular SABA treatment. The opposite result was seen for Gly16 homozygotes, who had an improved response to regular SABA treatment, with similar genotypespecific effects seen for several other clinical outcomes (FEV1, symptoms, and use of relief treatment). The investigators concluded that genotype at the ADRB2 Gly16Arg locus can affect long-term responses to regular treatment with SABAs.3 Results from several small studies60,61 have suggested that chronic use of LABAs with or without inhaled corticosteroids in Arg/Arg homozygotes is related to adverse outcomes, specifically worsening of lung function during treatment. However, analysis62 of a large clinical trial with a second replicate study population of patients with asthma treated with LABAs in combination with inhaled corticosteroids showed no effect of ADBR2 genotype on therapeutic responses including pulmonary function and asthma exacerbations. This finding strongly suggests that no adverse effects occur in patients with moderate to severe asthma receiving combination LABA and inhaled corticosteroid treatment. Furthermore, two prospective, genotype-stratified trials63,64 identified no major effect of Gly16Arg genotype on therapeutic responses to LABA treatment. One of these studies63 assessed a LABA alone and in combination with inhaled 409

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corticosteroids, and did not show any effect of Gly16Arg genotype on the response. This finding of no ADRB2 LABA pharmacogenetic effect represents important evidence-based data for asthma treatment; most patients with moderate to severe asthma are treated with a LABA in combination with an inhaled corticosteroid, as is recommended by clinical practice guidelines,65,66 the corticosteroid serving to inhibit any downregulation of β2 adrenoceptors. Such studies into responsiveness to β2-adrenoceptor agonist treatment and coding variants in ADBR2 stress the need for adequately powered pharmacogenetic analyses. These analyses should either be of a sufficient size that previous genotyping is not needed or should use genotype-stratified trials to ensure sufficient numbers of each genotype are represented. However, a limitation of genotype-stratified trials is that only a few genotypes can be prespecified for stratification, whereas many SNPs and genotypes could be important in determining drug responsiveness. Additionally, more extensive genetic studies to investigate the effects of variants in other genes in the β2-adrenoceptor agonist pathway on therapeutic or adverse responses are still needed. Furthermore, since adverse responses to β2-adrenoceptor agonists are rare, studies of rare genetic variants in ADBR2 and other pathway genes might help to define individuals at risk for rare severe events.67 A GWAS68 of acute reversibility to bronchodilators has identified SPATS2L as a novel gene that needs to be investigated further in future clinical trials of LABAs.

Corticosteroids Glucocorticoid steroids are currently the most widely used anti-inflammatory treatment for the management of persistent asthma. Much evidence suggests that regular use of inhaled corticosteroids improves asthma control and reduces asthma exacerbations.58 Chronic corticosteroid exposure is associated with systemic sideeffects, but these are reduced by inhaled delivery to the lung. In most cases, oral corticosteroids are used only for severe asthma exacerbations, although a subset of patients with severe asthma requires both high doses of inhaled and oral corticosteroids to achieve optimum asthma control.37,51 Despite the ubiquitous use of corticosteroids in asthma management, these drugs might not be equally effective in every patient, and most patients with severe asthma are classified as steroid resistant or refractory (once poor treatment adherence has been excluded).69,70 Several candidate gene studies have targeted different genes in the corticosteroid pathway, including one (STIP1) that encodes a protein that is part of the heterocomplex that activates the glucocorticoid receptor. Genetic variation of STIP1 has been associated with differences in FEV1 in response to inhaled corticosteroid treatment.71 Similar pharmacogenetic effects have been 410

reported with other candidate genes related to the corticosteroid pathway—eg, CRHR1 (corticotropin releasing hormone receptor 1).72 In a candidate gene study of 734 children with asthma,73 response to inhaled corticosteroids was studied in relation to genes involved in drug metabolism: CYP3A4, CYP3A5, and CYP3A7. 413 (56%) of the children were receiving an inhaled glucocorticoid daily, among whom polymorphisms in CYP3A5 and CYP3A7 were not associated with asthma control scores. However, asthma control scores were significantly improved in a small proportion of children with the CYP3A4*22 allele (which is associated with decreased hepatic CYP3A4 expression and activity) compared with those without this polymorphism. This finding emphasises the need for additional larger studies, possibly using a genotype-stratified trial design. Most pharmacogenetic studies in asthma have been based on candidate genes identified from relevant biological pathways. A GWAS74 for corticosteroid response in 418 asthmatics of white European descent identified the T gene as a novel asthma pharmacogenetic locus, with a two to three times difference in FEV1 response dependent on genotype (although the function of this gene is not yet known). Another GWAS10 has been done for response to corticosteroid treatment in asthma, first in a childhood cohort and then replicated in adult cohorts. Based on an analysis of half a million SNPs across the genome, a significant association was seen for genetic variants in the glucocorticoid-induced transcript gene, GLCCI1. This finding suggests the need for pharmacogenetic studies of response in children, who could have a different phenotype by being either naive to a treatment or on it for a shorter time period than adult patients. Several polymorphisms in this gene are strongly associated with each other (ie, are in linkage disequilibrium), making isolation of the specific functional variant difficult. Among corticosteroid-treated patients with asthma, variation in this gene accounted for 6·6% of the overall variability in clinical response to inhaled corticosteroids.10 Responses to corticosteroid treatment in asthma are probably affected by several genetic variants. Thus, the development of a genetic score will be important in helping to guide the selection of type and dose of corticosteroid treatment, especially for severe or difficult-to-treat cases. Such an application would represent a genetic approach to personalised medicine in these patients.

Leukotrienes Leukotriene-modifying drugs are used clinically as an adjunct treatment in patients with severe asthma, and in some counties as a primary treatment for childhood asthma. The two types of drug class available are the cysteinyl leukotriene receptor 1 antagonists and inhibitors of 5-lipoxygenase (encoded by ALOX5), the rate limiting enzyme in the cysteinyl leukotriene biosynthetic pathway.75–77 An early study78 showed that promoter www.thelancet.com/respiratory Vol 2 May 2014

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Biological drugs Personalisation of treatments on the basis of genotypic profiling is now becoming a reality for some diseases, especially cancers, although it is not yet applicable to asthma. However, several studies of new targeted treatments for asthma are currently underway and hold the promise of advancing personalised medicine approaches, including responder analyses based on pharmacogenetic parameters. For example, in a doseranging study82 of a biological drug (pitrakinra, a recombinant human interleukin 4 variant) that inhibits the shared interleukin 4 and interleukin 13 pathway, a significant dose-response effect on the primary endpoint of asthma exacerbation was seen only in individuals with a specific IL4R genotype (figure 4). The study showed that individuals with this genotype (roughly a third of the population) were responsive to the intervention (ie, www.thelancet.com/respiratory Vol 2 May 2014

30 GG

AG or AA p=–0·58

25

20 Exacerbations by treatment (%)

variation in ALOX5 identified a subset of patients with asthma who had reduced enzyme activity and poor response to treatment with 5-lipoxygenase pathway inhibitors. A study79 of ALOX5 variation in children with asthma showed that those who had more or less than five repeats of the SP1-binding motif GGGCGG in the gene’s promoter had increased urinary leukotriene E4 (the terminal cysteinyl leukotriene metabolite) concentrations, reduced baseline FEV1, and were less likely to be on cysteinyl leukotriene-directed treatment than were those with the wild-type genotype of five repeats. This study shows the importance of studying different racial groups, since variant alleles for this tandem repeat polymorphism occur at a much higher frequency in African-American children than in those of white European descent. Further studies are needed to assess whether African-Americans with a high degree of African ancestry at this locus are less responsive to this class of drugs than those with a lower degree of ancestry at this allele. Leukotriene signalling occurs through G-proteincoupled receptors encoded by two or possibly three genes: CYSLTR1 and CYSLTR2 (mediating the effects of leukotriene C4 and leukotriene D4, respectively), and OXGR1 (encoding GPR99, otherwise called P2Y15 or the oxoglutarate receptor, and mediating the effects of leukotriene E4).80,81 Previous candidate gene pharmacogenetic studies of CYSLTR1 and CYSLTR2 had conflicting results, emphasising the importance of welldesigned and adequately powered genetic studies. Pharmacogenetic studies are often difficult to do since they tend to use pre-existing data generated in clinical trials that were not designed to investigate these genetic outcomes. Both genetic and environmental differences in the populations that are intrinsic to clinical trials complicate interpretation of pharmacogenetic results and can lead to inconsistent findings. Issues such as sample size, generalisability, and replication that are crucial for genetic studies generally are especially important in pharmacogenetic studies.

p=–0·009 15

10

5

0

Placebo 1 mg 3 mg 10 mg (n=40) (n=37) (n=25) (n=32)

Placebo 1 mg 3 mg 10 mg (n=66) (n=65) (n=77) (n=63)

Figure 4: IL4R polymorphisms and reduced asthma exacerbations in response to treatment with an anti-interleukin 4 receptor antagonist (pitrakinra) Participants with the rs8832 GG genotype had a significant dose-dependent reduction in exacerbations; this effect was not seen in individuals with the AG or AA genotypes. Adapted from reference 82, by permission of Elsevier.

pitrakinra), whereas those expressing other genotypes were non-responsive. Unsurprisingly, the overall trial findings were negative, which shows the difficulty of identifying a responder subset that is embedded in a larger population that is unresponsive overall.75 Since the targeted biological drugs under development will be expensive, biomarkers such as those derived from pharmacogenetic approaches are needed to help to identify which patients are likely to be responsive to which specific interventions.

Conclusions In view of the contemporary changes in health care being led by oncology, the personalisation of medical care is becoming increasing important (panel 2). Such personalisation begins with strategies to prevent the development of disease and extends to appropriate individualised treatment and strategies to reduce further disease progression.83 Personalised medicine is based on 411

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Panel 2: Personalised medicine What is personalised medicine? • Personalised medicine is a new model of health care based on a patient’s unique characteristics; preventive measures and therapeutic options are tailored for each individual patient. • Individual patient characteristics include genetics and other predictive biomarkers, as well as demographic characteristics and environmental factors. • Prescribing the right preventive strategy or treatment for the right patient should have cost-effective outcomes. • Additional education is needed for health-care providers to implement personalised medicine in clinical practice. • This definition encompasses mechanistic pathway-targeted strategies for prevention and treatment, with appropriate companion diagnostics developed for the identification of relevant pathways. Role of genetics • Genetic variation affects disease risk (and therefore prevention strategies), progression, and response to treatment. • Pharmacogenetics is used to assess individual responses to drugs and susceptibility to side-effects. • Risk profiles or genetic scores for common and rare diseases are based on genetics and other individual characteristics. • Genetic risk profiles are better than family histories, which predict general rather than individual risk; family history is often negative, but non-informative because of small family size. • Genetic testing includes specific test for specific disease or responses to treatments, panels of tests for several diseases, and complete genome sequencing.

Search strategy and selection criteria We searched PubMed for articles published from Jan 1, 2000, to June 1, 2013, using the terms “asthma”, “genetics”, “epigenetics”, and “phenotype”. Only articles published in English, French, German, or Russian were included. We selected articles on the basis of the size of the populations studied, the uniqueness of findings, and the relevance to the topic of the Review.

individual characteristics such as age, body-mass index, race, and local environment, with incorporation of predictive biomarkers including genetic profiles or scores that combine several risk-associated genetic variants. This approach will be cost-effective because it will be possible to tailor the extent of preventive screening to the individual instead of using screening based on general health guidelines.84 Health-care professionals should understand that many of the biomarkers, including genetic markers, will not be diagnostic, but will provide risk estimates to guide the screening process and preventive strategies, while pharmacogenetic profiles will help to determine individualised treatments.85 With respect to asthma susceptibility, genetic scores will provide an estimate of the risk of developing asthma based on combinations of genetic variants in multiple genes. Specific approaches will include additive models 412

and more complicated statistical models (eg, weighting of genetic variants on the basis of risk ratios for each variant). A legitimate question to ask is how a personalised approach could be used by health-care professionals and their patients to guide disease management. For example, when a mother with asthma brings in her young child with an upper-respiratory infection, the physician is immediately concerned that this child might develop wheezing and subsequently clinical asthma. Although such children are generally at an increased risk of developing asthma because of a family history of the disease, this individual child might be at either high or at low risk based on their individual genetic score (as might a child without a positive family history). If the child is at high risk of developing asthma, appropriate preventive measures including treatment for upper-respiratory infections, early use of anti-asthma drugs, and comprehensive environmental modifications might be indicated and discussed with the family.86 Importantly, a negative family history is often non-informative because of small family sizes (panel 2). Additionally, a parent might be genetically at risk for asthma but fortunate not to have encountered environmental triggers, thereby avoiding the disease (although if testing was done, such a parent might have some characteristics of asthma such as bronchial hyperresponsiveness). Therefore, genetic profiles can be important even in the absence of a family history. For asthma progression and severity, individual genetic scores can be calculated for loss of lung function by use of a combination of genetic variants (which differ from the variants for asthma susceptibility). This risk estimate for the development of severe disease will allow the healthcare provider to prescribe drugs and advise on environmental changes (eg, avoidance of active and passive smoke exposure, weight loss, and avoidance of known triggers for asthma exacerbations). Although a healthy lifestyle is recommended for everyone, health issues such as smoking and obesity are widespread. Whether knowledge of individual increased risk for the development of severe asthma will be an important stimulus for specific behavioural change remains to be seen. However, with the development of specific biological drugs that, for example, disrupt allergic mechanisms (anti-immunoglobulin E, Th2 cytokines, chemokines, etc), understanding the importance of biological pathways in an individual patient will be necessary to guide personalised treatment.87 Individual response to treatment could in the future be predicted on the basis of genetic testing, which will be especially important for the use of biological drugs in patients with severe disease. Such testing could avoid the need for patients to be exposed to costly biological drugs for long trial periods in order to identify pathway-specific therapeutic responsiveness. This strategy differs from typical pharmacogenetics based on at-risk gene variants because, in this approach, genetics is used to identify individuals with asthma whose disease is regulated by specific pathways that can be treated with specific drugs. www.thelancet.com/respiratory Vol 2 May 2014

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Specific genetic tests and panels of tests have already been developed for both rare and common diseases. Complete genome sequencing is now being offered at several health-care facilities, resulting in personal risk profiles for the development of diseases ranging from respiratory diseases to cancers. However, most predictions for diseases such as asthma are based on single genetic variants. Until genetic profiles that use multiple associated variants are further developed and tested for validity in new cohorts, the low accuracy of such predictions limits their clinical usefulness. However, research into such approaches is progressing rapidly and such techniques are already clinically useful for other diseases. Clinicians and researchers should keep abreast of the latest findings for their diseases of interest. Any proposed tests must be critically examined for accuracy and should be updated continuously as new genetic variants are reported.88 Placing the individual citizen at the centre of health care is the basis for personalised medicine, and asthma is an excellent example of where genomic analysis is already able to affect therapeutic decisions.83,84 However, further rapid advance will depend on a collaborative approach to research so that large collections of extensively phenotyped patients and associated biological collections are generated from different countries. Such work should embrace close interactions between patients, biological researchers, clinicians, information specialists, and industry. Data from drug trials should be made available for pharmacogenetic analysis and new methods of drug target discovery should be developed. Existing collaborative efforts between industry and academia include the European Union UBIOPRED project and the US National Heart, Lung, and Blood Institute’s Severe Asthma Research Program. It has taken more than 60 years since the structure of DNA was first uncovered for us to begin to realise the medical value of understanding the genetic code as relevant to the everyday practice of medicine. Asthma is a good example of how personal genetic information is now beginning to inform disease management. The challenge will be to harness such information in an integrated way, together with the mass of data being collected from other sources, to create the knowledge necessary to deliver truly personalised health care. Contributors All authors contributed equally.

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Declaration of interests We declare that we have no competing interests. Acknowledgments For DAM and ERB, this work was supported by grants from the US National Institutes of Health (HL109164, HL65899, and NR013700). STH is a UK Medical Research Professor. References 1 National Institutes of Health. Genetic testing for cystic fibrosis. NIH Consens Statement 1997; 15: 1–37. 2 Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860–921.

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For the UBIOPRED project see http://www.imi.europa.eu/ content/u-biopred For the Severe Asthma Research Program see http://www. severeasthma.org

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