Predicting response to short-acting bronchodilator medication using Bayesian networks

Multi-omic approach associates blood methylome with bronchodilator drug response in pediatric asthma

BACKGROUND

Albuterol is the drug most widely used as asthma treatment among African Americans despite having a lower bronchodilator drug response (BDR) than other populations. Although BDR is affected by gene and environmental factors, the influence of DNA methylation is unknown.

OBJECTIVE

This study aimed to identify epigenetic markers in whole blood associated with BDR, study their functional consequences by multi-omic integration, and assess their clinical applicability in admixed populations with a high asthma burden.

METHODS

We studied 414 children and young adults (8-21 years old) with asthma in a discovery and replication design. We performed an epigenome-wide association study on 221 African Americans and replicated the results on 193 Latinos. Functional consequences were assessed by integrating epigenomics with genomics, transcriptomics, and environmental exposure data. Machine learning was used to develop a panel of epigenetic markers to classify treatment response.

RESULTS

We identified 5 differentially methylated regions and 2 CpGs genome-wide significantly associated with BDR in African Americans located in FGL2 (cg08241295, P = 6.8 × 10-9) and DNASE2 (cg15341340, P = 7.8 × 10-8), which were regulated by genetic variation and/or associated with gene expression of nearby genes (false discovery rate < 0.05). The CpG cg15341340 was replicated in Latinos (P = 3.5 × 10-3). Moreover, a panel of 70 CpGs showed good classification for those with response and nonresponse to albuterol therapy in African American and Latino children (area under the receiver operating characteristic curve for training, 0.99; for validation, 0.70-0.71). The DNA methylation model showed similar discrimination as clinical predictors (P > .05).

CONCLUSIONS

We report novel associations of epigenetic markers with BDR in pediatric asthma and demonstrate for the first time the applicability of pharmacoepigenetics in precision medicine of respiratory diseases.

Survivability modelling using Bayesian network for patients with first and secondary primary cancers

BACKGROUND AND OBJECTIVE

Multiple primary cancers significantly threat patient survivability. Predicting the survivability of patients with two cancers is challenging because its stochastic pattern relates with numerous variables.

METHODS

In this study, a Bayesian network (BN) model was proposed to describe the occurrence of two primary cancers and predict the five-year survivability of patients using probabilistic evidence. Eleven types of major primary cancers and contingent occurrences of secondary cancers were investigated. A nationwide two-cancer database involving 7,845 patients in Taiwan was investigated. The BN topology is rigorously examined and imbalanced dataset is processed by the synthetic minority oversampling technique. The proposed BN survivability prognosis model was compared with benchmark approaches.

RESULTS

The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group.

CONCLUSIONS

Using the proposed BN model, one can estimate the posterior probabilities for every query provided appropriate prior evidences. The potential survivability information of patients, treatment effects, and socio-demographics factor effects predicted by the proposed model can help in cancer treatment assessment and cancer development monitoring.

Assessment of genetic factor and depression interactions for asthma symptom severity in cohorts of childhood and elderly asthmatics

It is well known that depression is associated with asthma symptoms. We assessed the combined effects of genetic factors and depression on asthma symptom severity using Bayesian network (BN) analysis. The common 100 top-ranked single-nucleotide polymorphisms (SNPs) were obtained from two genome-wide association studies of symptom severity in two childhood asthmatics trials (CAMP (Childhood Asthma Management Program) and CARE (Childhood Asthma Research and Education)). Using SNPs plus five discretized variables (depression, anxiety, age, sex, and race), we performed BN analysis in 529 CAMP subjects. We identified two nodes (depression and rs4672619 mapping to ERBB4 (Erb-B2 receptor tyrosine kinase 4)) that were within the Markov neighborhood of the symptom node in the network and then evaluated the interactive effects of depressive status and rs4672619 genotypes on asthma symptom severity. In childhood asthmatics with homozygous reference alleles, severe depression was related to less severe symptoms. However, in childhood asthmatics with heterozygous alleles and homozygous variant alleles, depression and symptom severity showed a positive correlation (interaction permutation P value = 0.019). We then tried to evaluate whether the interactive effects that we found were sustained in another independent cohort of elderly asthmatics. Contrary to the findings from childhood asthmatics, elderly asthmatics with homozygous reference alleles showed a positive correlation between depression and symptom severity, and elderly asthmatics with heterozygous alleles and homozygous variant alleles showed a negative correlation (interaction permutation P value = 0.003). In conclusion, we have identified a novel SNP, rs4672619, that shows interactive effects with depression on asthma symptom severity in childhood and elderly asthmatics in opposite directions.

Pharmacogenomic test that predicts response to β2-agonists in adults with asthma is cost effective

BACKGROUND

Pharmacogenomic tests that predict which asthma patients are likely to respond to β₂-agonists hold promise to improve care for asthma.

OBJECTIVE

To identify the clinical and economic circumstances under which a pharmacogenomic test that predicts response to β₂-agonists might or might not be an appropriate, cost-effective option.

METHODS

We synthesized published data on clinical and economic outcomes in adults 18-35 to project 10-year costs, quality-adjusted life years and cost-effectiveness of pharmacogenomic testing for β₂-agonist response.

RESULTS

Pharmacogenomic testing for β₂-agonist response conferred a cost-effectiveness ratio of $13,700 per quality-adjusted life year gained compared with no testing.

CONCLUSION

Pharmacogenomic testing for β₂-agonist response in individuals with asthma is potentially cost effective and should be pursued by test developers.

Asthma Pharmacogenomics: 2015 Update

There is evidence that genetic factors are implicated in the observed differences in therapeutic responses to the common classes of asthma therapy such as β2-agonists, corticosteroids, and leukotriene modifiers. Pharmacogenomics explores the roles of genetic variation in drug response and continues to be a field of great interest in asthma therapy. Prior studies have focused on candidate genes and recently emphasized genome-wide association analyses. Newer integrative omics and system-level approaches have recently revealed novel understanding of drug response pathways. However, the current known genetic loci only account for a fraction of variability in drug response and ongoing research is needed. While the field of asthma pharmacogenomics is not yet fully translatable to clinical practice, ongoing research should hopefully achieve this goal in the near future buttressed by the recent precision medicine efforts in the USA and worldwide.

Asthma pharmacogenetics and the development of genetic profiles for personalized medicine

Human genetics research will be critical to the development of genetic profiles for personalized or precision medicine in asthma. Genetic profiles will consist of gene variants that predict individual disease susceptibility and risk for progression, predict which pharmacologic therapies will result in a maximal therapeutic benefit, and predict whether a therapy will result in an adverse response and should be avoided in a given individual. Pharmacogenetic studies of the glucocorticoid, leukotriene, and β₂-adrenergic receptor pathways have focused on candidate genes within these pathways and, in addition to a small number of genome-wide association studies, have identified genetic loci associated with therapeutic responsiveness. This review summarizes these pharmacogenetic discoveries and the future of genetic profiles for personalized medicine in asthma. The benefit of a personalized, tailored approach to health care delivery is needed in the development of expensive biologic drugs directed at a specific biologic pathway. Prior pharmacogenetic discoveries, in combination with additional variants identified in future studies, will form the basis for future genetic profiles for personalized tailored approaches to maximize therapeutic benefit for an individual asthmatic while minimizing the risk for adverse events.

Pharmacogenomics in asthma therapy: where are we and where do we go?

The response to drug treatment in asthma is a complex trait and is markedly variable even in patients with apparently similar clinical features. Pharmaco-genomics, which is the study of variations of human genome characteristics as related to drug response, can play a role in asthma therapy. Both a traditional candidate-gene approach to conducting genetic association studies and genome-wide association studies have provided an increasing list of genes and variants associated with the three major classes of asthma medications: β2-agonists, inhaled corticosteroids, and leukotriene modifiers. Moreover, a recent integrative, systems-level approach has offered a promising opportunity to identify important pharmacogenomics loci in asthma treatment. However, we are still a long way away from making this discipline directly relevant to patients. The combination of network modeling, functional validation, and integrative omics technologies will likely be needed to move asthma pharmacogenomics closer to clinical relevance.

Pharmacogenomic characterization of gemcitabine response--a framework for data integration to enable personalized medicine

Supplemental Digital Content is available in the text.

Objectives

Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic.

Methods

We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response.

Results

Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated.

Conclusion

Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.

Pharmacogenetics of beta2 adrenergic receptor agonists in asthma management

Beta2 (β2) adrenergic receptor agonists (beta agonists) are a commonly prescribed treatment for asthma despite the small increase in risk for life-threatening adverse responses associated with long-acting beta agonist (LABA). The concern for life-threatening adverse effects associated with LABA and the inter-individual variability of therapeutic responsiveness to LABA-containing combination therapies provide the rationale for pharmacogenetic studies of beta agonists. These studies primarily evaluated genes within the β2-adrenergic receptor and related pathways; however, recent genome-wide studies have identified novel loci for beta agonist response. Recent studies have identified a role for rare genetic variants in determining beta agonist response and, potentially, the risk for rare, adverse responses to LABA. Before genomics research can be applied to the development of genetic profiles for personalized medicine, it will be necessary to continue adapting to the analysis of an increasing volume of genetic data in larger cohorts with a combination of analytical methods and in vitro studies.

Pharmacogenetics: implications of race and ethnicity on defining genetic profiles for personalized medicine

Pharmacogenetics is being used to develop personalized therapies specific to subjects from different ethnic or racial groups. To date, pharmacogenetic studies have been primarily performed in trial cohorts consisting of non-Hispanic white subjects of European descent. A "bottleneck" or collapse of genetic diversity associated with the first human colonization of Europe during the Upper Paleolithic period, followed by the recent mixing of African, European, and Native American ancestries, has resulted in different ethnic groups with varying degrees of genetic diversity. Differences in genetic ancestry might introduce genetic variation, which has the potential to alter the therapeutic efficacy of commonly used asthma therapies, such as β2-adrenergic receptor agonists (β-agonists). Pharmacogenetic studies of admixed ethnic groups have been limited to small candidate gene association studies, of which the best example is the gene coding for the receptor target of β-agonist therapy, the β2-adrenergic receptor (ADRB2). Large consortium-based sequencing studies are using next-generation whole-genome sequencing to provide a diverse genome map of different admixed populations, which can be used for future pharmacogenetic studies. These studies will include candidate gene studies, genome-wide association studies, and whole-genome admixture-based approaches that account for ancestral genetic structure, complex haplotypes, gene-gene interactions, and rare variants to detect and replicate novel pharmacogenetic loci.

Gene-based association identifies SPATA13-AS1 as a pharmacogenomic predictor of inhaled short-acting beta-agonist response in multiple population groups

Inhaled short-acting beta-agonist (SABA) medication is commonly used in asthma patients to rapidly reverse airway obstruction and improve acute symptoms. We performed a genome wide association study of SABA medication response using gene-based association tests. A linear mixed model approach was first used for SNP associations, and results were later combined using GATES to generate gene-based associations. Our results identified SPATA13-AS1 as being significantly associated with SABA bronchodilator response in 328 healthy African Americans. In replication, this gene was associated with SABA response among 2 separate groups of African Americans with asthma (n=1,073, p=0.011 and n=1,968, p=0.014), 149 healthy African Americans (p=0.003), and 556 European Americans with asthma (p=0.041). SPATA13-AS1 was also associated with longitudinal SABA medication usage in 2 separate groups of African Americans with asthma (n=658, p=0.047 and n=1,968, p=0.025). Future studies are needed to delineate the precise mechanism by which SPATA13-AS1 may influence SABA response.

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

Many genetic variants have been shown to affect drug response through changes in drug efficacy and likelihood of adverse effects. Much of pharmacogenomic science has focused on discovering and clinically implementing single gene variants with large effect sizes. Given the increasing complexities of drug responses and their variability, a systems approach may be enabling for discovery of new biology in this area. Further, systems approaches may be useful in addressing challenges in moving these data to clinical implementation, including creation of predictive models of drug response phenotypes, improved clinical decision-making through complex biological models, improving strategies for integrating genomics into clinical practice, and evaluating the impact of implementation programs on public health.

Integrative systems biology approaches in asthma pharmacogenomics

In order to improve therapeutic outcomes, there is a tremendous need to identify patients who are likely to respond to a given asthma treatment. Pharmacogenomic studies have explained a portion of the variability in drug response and provided an increasing list of candidate genes and SNPs. However, as phenotypic variation arises from a network of complex interactions among genetic and environmental factors, rather than individual genes or SNPs, a multidisciplinary, systems-level approach is required in order to understand the inter-relationships among these factors. Systems biology, which seeks to capture interactions between genetic factors and other variables, offers a promising approach to improved therapeutic outcomes in asthma. This aritcle will review and update progress in the pharmacogenomics of asthma and then discuss the application of systems biology approaches to asthma pharmacogenomics.

Predicting inhaled corticosteroid response in asthma with two associated SNPs

Inhaled corticosteroids are the most commonly used controller medications prescribed for asthma. Two single-nucleotide polymorphisms (SNPs), rs1876828 in CRHR1 and rs37973 in GLCCI1, have previously been associated with corticosteroid efficacy. We studied data from four existing clinical trials of asthmatics who received inhaled corticosteroids and had lung function measured by forced expiratory volume in one second (FEV1) before and after the period of such treatment. We combined the two SNPs rs37973 and rs1876828 into a predictive test of FEV1 change using a Bayesian model, which identified patients with good or poor steroid response (highest or lowest quartile, respectively) with predictive performance of 65.7% (p = 0.039 vs. random) area under the receiver-operator characteristic curve in the training population and 65.9% (p = 0.025 vs. random) in the test population. These findings show that two genetic variants can be combined into a predictive test that achieves similar accuracy and superior replicability compared with single SNP predictors.

Systems biology data analysis methodology in pharmacogenomics

Pharmacogenetics aims to elucidate the genetic factors underlying the individual's response to pharmacotherapy. Coupled with the recent (and ongoing) progress in high-throughput genotyping, sequencing and other genomic technologies, pharmacogenetics is rapidly transforming into pharmacogenomics, while pursuing the primary goals of identifying and studying the genetic contribution to drug therapy response and adverse effects, and existing drug characterization and new drug discovery. Accomplishment of both of these goals hinges on gaining a better understanding of the underlying biological systems; however, reverse-engineering biological system models from the massive datasets generated by the large-scale genetic epidemiology studies presents a formidable data analysis challenge. In this article, we review the recent progress made in developing such data analysis methodology within the paradigm of systems biology research that broadly aims to gain a 'holistic', or 'mechanistic' understanding of biological systems by attempting to capture the entirety of interactions between the components (genetic and otherwise) of the system.

The pharmacogenetics and pharmacogenomics of asthma therapy

Despite the availability of several classes of asthma medications and their overall effectiveness, a significant portion of patients fail to respond to these therapeutic agents. Evidence suggests that genetic factors may partly mediate the heterogeneity in asthma treatment response. This review discusses important findings in asthma pharmacogenetic and pharmacogenomic studies conducted to date, examines limitations of these studies and, finally, proposes future research directions in this field. The focus will be on the three major classes of asthma medications: β-adrenergic receptor agonists, inhaled corticosteroids and leukotriene modifiers. Although many studies are limited by small sample sizes and replication of the findings is needed, several candidate genes have been identified. High-throughput technologies are also allowing for large-scale genetic investigations. Thus, the future is promising for a personalized treatment of asthma, which will improve therapeutic outcomes, minimize side effects and lead to a more cost-effective care.

Marco Ramoni: an appreciation of academic achievement

We review the scholarly career of our colleague, Marco Ramoni, who died unexpectedly in the summer of 2010. His work mainly explored the development and application of Bayesian techniques to model clinical, public health, and bioinformatics questions. His contributions have led to improvements in our ability to model behavior that evolves in time, to explore systematic relationships among large sets of covariates, and to tease out the meaning of data on the role of genetic variation in the genesis of important diseases.

Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement

Abilities to successfully quit smoking display substantial evidence for heritability in classic and molecular genetic studies. Genome-wide association (GWA) studies have demonstrated single-nucleotide polymorphisms (SNPs) and haplotypes that distinguish successful quitters from individuals who were unable to quit smoking in clinical trial participants and in community samples. Many of the subjects in these clinical trial samples were aided by nicotine replacement therapy (NRT). We now report novel GWA results from participants in a clinical trial that sought dose/response relationships for "precessation" NRT. In this trial, 369 European-American smokers were randomized to 21 or 42 mg NRT, initiated 2 wks before target quit dates. Ten-week continuous smoking abstinence was assessed on the basis of self-reports and carbon monoxide levels. SNP genotyping used Affymetrix 6.0 arrays. GWA results for smoking cessation success provided no P value that reached "genome-wide" significance. Compared with chance, these results do identify (a) more clustering of nominally positive results within small genomic regions, (b) more overlap between these genomic regions and those identified in six prior successful smoking cessation GWA studies and (c) sets of genes that fall into gene ontology categories that appear to be biologically relevant. The 1,000 SNPs with the strongest associations form a plausible Bayesian network; no such network is formed by randomly selected sets of SNPs. The data provide independent support, based on individual genotyping, for many loci previously nominated on the basis of data from genotyping in pooled DNA samples. These results provide further support for the idea that aid for smoking cessation may be personalized on the basis of genetic predictors of outcome.

Methodological and statistical issues in pharmacogenomics

Pharmacogenomics strives to explain the interindividual variability in response to drugs due to genetic variation. Although technological advances have provided us with relatively easy and cheap methods for genotyping, promises about personalised medicine have not yet met our high expectations. Successful results that have been achieved within the field of pharmacogenomics so far are, to name a few, HLA-B*5701 screening to avoid hypersensitivity to the antiretroviral abacavir, thiopurine S-methyltransferase (TPMT) genotyping to avoid thiopurine toxicity, and CYP2C9 and VKORC1 genotyping for better dosing of the anticoagulant warfarin. However, few pharmacogenetic examples have made it into clinical practice in the treatment of complex diseases. Unfortunately, lack of reproducibility of results from observational studies involving many genes and diseases seems to be a common pattern in pharmacogenomic studies. In this article we address some of the methodological and statistical issues within study design, gene and single nucleotide polymorphism (SNP) selection and data analysis that should be considered in future pharmacogenomic research. First, we discuss some of the issues related to the design of epidemiological studies, specific to pharmacogenomic research. Second, we describe some of the pros and cons of a candidate gene approach (including gene and SNP selection) and a genome-wide scan approach. Finally, conventional as well as several innovative approaches to the analysis of large pharmacogenomic datasets are proposed that deal with the issues of multiple testing and systems biology in different ways.

The role of pharmacogenomics in improving the management of asthma

There is a large amount of interindividual variability in both therapeutic and adverse responses to asthma therapies. Genetic variability can account for 50% to 60% of this variability. Pharmacogenomics holds out the promise of allowing clinicians to prospectively choose therapies that have the greatest likelihood to be effective for individual patients and to avoid those that might have a high likelihood of producing adverse effects. In this article we review the principles of pharmacogenomic investigation. We explore the data developed from the early pharmacogenomic studies with the most common asthma therapies. Furthermore, we explore the potential use of pharmacogenomics, as well as caveats in interpreting such information.