An integrative systems biology approach to understanding pulmonary diseases

Stratification of asthma by lipidomic profiling of induced sputum supernatant

BACKGROUND

Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features.

OBJECTIVE

We performed a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as from healthy controls.

METHODS

Induced sputum supernatant was collected from 211 adults with asthma and 41 healthy individuals enrolled onto the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study. Sputum lipidomes were characterized by semiquantitative shotgun mass spectrometry and clustered using topologic data analysis to identify lipid phenotypes.

RESULTS

Shotgun lipidomics of induced sputum supernatant revealed a spectrum of 9 molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthma patients and healthy controls, but also within the asthma patient population. Matching clinical, pathobiologic, proteomic, and transcriptomic data helped inform the underlying disease processes. Sputum lipid phenotypes with higher levels of nonendogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts.

CONCLUSION

We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthma patients resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator.

Systems Biology in Asthma

The application of mathematical and computational analysis, together with the modelling of biological and physiological processes, is transforming our understanding of the pathophysiology of complex diseases. This systems biology approach incorporates large amounts of genomic, transcriptomic, proteomic, metabolomic, breathomic, metagenomic and imaging data from disease sites together with deep clinical phenotyping, including patient-reported outcomes. Integration of these datasets will provide a greater understanding of the molecular pathways associated with severe asthma in each individual patient and determine their personalised treatment regime. This chapter describes some of the data integration methods used to combine data sets and gives examples of the results obtained using single datasets and merging of multiple datasets (data fusion and data combination) from several consortia including the severe asthma research programme (SARP) and the Unbiased Biomarkers Predictive of Respiratory Disease Outcomes (U-BIOPRED) consortia. These results highlight the involvement of several different immune and inflammatory pathways and factors in distinct subsets of patients with severe asthma. These pathways often overlap in patients with distinct clinical features of asthma, which may explain the incomplete or no response in patients undergoing specific targeted therapy. Collaboration between groups will improve the predictions obtained using a systems medicine approach in severe asthma.

Clinical and transcriptomic features of persistent exacerbation-prone severe asthma in U-BIOPRED cohort

Background

Exacerbation‐prone asthma is a feature of severe disease. However, the basis for its persistency remains unclear.

Objectives

To determine the clinical and transcriptomic features of frequent exacerbators (FEs) and persistent FEs (PFEs) in the U‐BIOPRED cohort.

Methods

We compared features of FE (≥2 exacerbations in past year) to infrequent exacerbators (IE, <2 exacerbations) and of PFE with repeat ≥2 exacerbations during the following year to persistent IE (PIE). Transcriptomic data in blood, bronchial and nasal epithelial brushings, bronchial biopsies and sputum cells were analysed by gene set variation analysis for 103 gene signatures.

Results

Of 317 patients, 62.4% had FE, of whom 63.6% had PFE, while 37.6% had IE, of whom 61.3% had PIE. Using multivariate analysis, FE was associated with short‐acting beta‐agonist use, sinusitis and daily oral corticosteroid use, while PFE was associated with eczema, short‐acting beta‐agonist use and asthma control index. CEA cell adhesion molecule 5 (CEACAM5) was the only differentially expressed transcript in bronchial biopsies between PE and IE. There were no differentially expressed genes in the other four compartments. There were higher expression scores for type 2, T‐helper type‐17 and type 1 pathway signatures together with those associated with viral infections in bronchial biopsies from FE compared to IE, while there were higher expression scores of type 2, type 1 and steroid insensitivity pathway signatures in bronchial biopsies of PFE compared to PIE.

Conclusion

The FE group and its PFE subgroup are associated with poor asthma control while expressing higher type 1 and type 2 activation pathways compared to IE and PIE, respectively.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

Asthma and COVID-19: lessons learned and questions that remain

INTRODUCTION

Asthma is one of the most common chronic diseases worldwide. As a disease of the respiratory tract, the site of entry for the SARS-CoV-2 virus, there may be an important interplay between asthma and COVID-19 disease.

AREAS COVERED

We report asthma prevalence among hospitalized cohorts with COVID-19. Those with non-allergic and severe asthma may be at increased risk of a worsened clinical outcome from COVID-19 infection. We explore the epidemiology of asthma as a risk factor for the severity of COVID-19 infection. We then consider the role COVID-19 may play in leading to exacerbations of asthma. The impact of asthma endotype on outcome is discussed. Lastly, we address the safety of common asthma therapeutics. A literature search was performed with relevant terms for each of the sections of the review using PubMed, Google Scholar, and Medline.

EXPERT OPINION

Asthma diagnosis may be a risk factor for severe COVID-19 especially for those with severe disease or nonallergic phenotypes. COVID-19 does not appear to provoke asthma exacerbations and asthma therapeutics should be continued for patients with exposure to COVID-19. Clearly much regarding this topic remains unknown and we identify some key questions that may be of interest for future researchers.[Figure: see text].

Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0

BACKGROUND

To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses.

METHODS

Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework.

RESULTS

A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma.

CONCLUSIONS

In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.

Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients

Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10⁻³ for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

Three Major Efforts to Phenotype Asthma: Severe Asthma Research Program, Asthma Disease Endotyping for Personalized Therapeutics, and Unbiased Biomarkers for the Prediction of Respiratory Disease Outcome

The SARP, ADEPT, and U-BIOPRED programs are all significant efforts in characterizing asthma and reporting clusters that will assist in designing personalized therapies for asthma, and especially severe asthma. Key aspects of the design of these programs are summarized and major findings are reported in this review.

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

Priorities for future research into asthma diagnostic tools: A PAN-EU consensus exercise from the European asthma research innovation partnership (EARIP)

The diagnosis of asthma is currently based on clinical history, physical examination and lung function, and to date, there are no accurate objective tests either to confirm the diagnosis or to discriminate between different types of asthma. This consensus exercise reviews the state of the art in asthma diagnosis to identify opportunities for future investment based on the likelihood of their successful development, potential for widespread adoption and their perceived impact on asthma patients. Using a two-stage e-Delphi process and a summarizing workshop, a group of European asthma experts including health professionals, researchers, people with asthma and industry representatives ranked the potential impact of research investment in each technique or tool for asthma diagnosis and monitoring. After a systematic review of the literature, 21 statements were extracted and were subject of the two-stage Delphi process. Eleven statements were scored 3 or more and were further discussed and ranked in a face-to-face workshop. The three most important diagnostic/predictive tools ranked were as follows: "New biological markers of asthma (eg genomics, proteomics and metabolomics) as a tool for diagnosis and/or monitoring," "Prediction of future asthma in preschool children with reasonable accuracy" and "Tools to measure volatile organic compounds (VOCs) in exhaled breath."

Pathophysiological Mechanisms of Asthma

The recent Lancet commission has highlighted that "asthma" should be used to describe a clinical syndrome of wheeze, breathlessness, chest tightness, and sometimes cough. The next step is to deconstruct the airway into components of fixed and variable airflow obstruction, inflammation, infection and altered cough reflex, setting the airway disease in the context of extra-pulmonary co-morbidities and social and environmental factors. The emphasis is always on delineating treatable traits, including variable airflow obstruction caused by airway smooth muscle constriction (treated with short- and long-acting β-2 agonists), eosinophilic airway inflammation (treated with inhaled corticosteroids) and chronic bacterial infection (treated with antibiotics with benefit if it is driving the disease). It is also important not to over-treat the untreatable, such as fixed airflow obstruction. These can all be determined using simple, non-invasive tests such as spirometry before and after acute administration of a bronchodilator (reversible airflow obstruction); peripheral blood eosinophil count, induced sputum, exhaled nitric oxide (airway eosinophilia); and sputum or cough swab culture (bacterial infection). Additionally, the pathophysiology of risk domains must be considered: these are risk of an asthma attack, risk of poor airway growth, and in pre-school children, risk of progression to eosinophilic school age asthma. Phenotyping the airway will allow more precise diagnosis and targeted treatment, but it is important to move to endotypes, especially in the era of increasing numbers of biologicals. Advances in -omics technology allow delineation of pathways, which will be particularly important in TH2 low eosinophilic asthma, and also pauci-inflammatory disease. It is very important to appreciate the difficulties of cluster analysis; a patient may have eosinophilic airway disease because of a steroid resistant endotype, because of non-adherence to basic treatment, and a surge in environmental allergen burden. Sophisticated -omics approaches will be reviewed in this manuscript, but currently they are not being used in clinical practice. However, even while they are being evaluated, management of the asthmas can and should be improved by considering the pathophysiologies of the different airway diseases lumped under that umbrella term, using simple, non-invasive tests which are readily available, and treating accordingly.

Identification and prospective stability of electronic nose (eNose)-derived inflammatory phenotypes in patients with severe asthma

BACKGROUND

Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes.

OBJECTIVES

We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes.

METHODS

In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability.

RESULTS

Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045).

CONCLUSIONS

We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma.

Translating Asthma: Dissecting the Role of Metabolomics, Genomics and Personalized Medicine

The management of asthma has largely stagnated over the last 25 years, but we are at the dawning of a new age wherein -omics technology can help us manage the disease objectively and rationally. Even in this new scientific age, getting the basics of asthma management right remains essential. The new technologies which can be applied to multiple biological samples include genomics (study of the genome), transcriptomics (gene transcription), lipidomics, proteomics and metabolomics (lipids, proteins and metabolites, respectively) and breathomics, using exhaled breath as a source of biomarkers, which is of particular interest in view of its non-invasive nature in pediatrics. Important applications will include the diagnosis of airways disease, including its components; the pathways driving airway pathology; monitoring the response to treatment; and measuring future risk (asthma attacks, poor lung growth trajectory). With the advent of a wide range of novel biologicals to treat asthma, -omics technology to personalize therapy will be especially important. The U-BIOPRED (Europe) and SARP (USA) groups have been most active in this field, especially using bronchoscopically obtained samples to perform cluster analyses to define new asthma endotypes. However, stability over time and consistency between investigators is imperfect. This is perhaps unsurprising; results of biomarker studies in asthma will be a composite of the underlying disease, the (variable) effects of adverse drivers such as allergen exposure and pollution, the effects of treatment, and the effects of adherence or otherwise to treatment. Ultimately, the aim should be an exhaled breath based tool with a rapid result that can be used as a routine in the clinic. However, at the moment, there are as yet no clinical applications in children of -omics technology.

Current Approaches for Phenotyping as a Target for Precision Medicine in COPD Management

The study of airway diseases continues to present several challenges for modern medicine. The different disease presentations with variables and overlapping features may result in a real challenge for the clinician. In this context, the concept of precision medicine has started to emerge in order to give answers to some of these challenges from a diagnostic and therapeutic point of view. The main reasons to target for precision medicine in chronic obstructive pulmonary disease (COPD) include that there is variability in the clinical presentation, there is no correlation between the different clinical variables at the patient level, there are a number of relevant clinical variables associated with outcomes, we do have specific therapies for specific patient types, and that there is variability in the clinical response to different therapies. To bring precision medicine into clinical practice several approaches have been used, including the use of independent variables to identify subjects, the use of multidimensional indexes, the so-called clinical phenotypes, and the approximation by the so-called treatable traits. All these approaches have their strengths and weaknesses which are reviewed in the present document. Although there is no universally accepted proposal, the available initiatives provide us with a framework on which to start working and move toward precision medicine in COPD, with the ultimate goal of bringing the best possible medicine to each patient in particular.

Phenotyping of Chronic Obstructive Pulmonary Disease Based on the Integration of Metabolomes and Clinical Characteristics

Apart from the refined management-oriented clinical stratification of chronic obstructive pulmonary disease (COPD), the molecular pathologies behind this highly prevalent disease have remained obscure. The aim of this study was the characterization of patients with COPD, based on the metabolomic profiling of peripheral blood and exhaled breath condensate (EBC) within the context of defined clinical and demographic variables. Mass-spectrometry-based targeted analysis of serum metabolites (mainly amino acids and lipid species), untargeted profiles of serum and EBC of patients with COPD of different clinical characteristics (n = 25) and control individuals (n = 21) were performed. From the combined clinical/demographic and metabolomics data, associations between clinical/demographic and metabolic parameters were searched and a de novo phenotyping for COPD was attempted. Adjoining the clinical parameters, sphingomyelins were the best to differentiate COPD patients from controls. Unsaturated fatty acid-containing lipids, ornithine metabolism and plasma protein composition-associated signals from the untargeted analysis differentiated the Global Initiative for COPD (GOLD) categories. Hierarchical clustering did not reveal a clinical-metabolomic stratification superior to the strata set by the GOLD consensus. We conclude that while metabolomics approaches are good for finding biomarkers and clarifying the mechanism of the disease, there are no distinct co-variate independent clinical-metabolic phenotypes.

ARDS: challenges in patient care and frontiers in research

This review discusses the clinical challenges associated with ventilatory support and pharmacological interventions in patients with acute respiratory distress syndrome (ARDS). In addition, it discusses current scientific challenges facing researchers when planning and performing trials of ventilatory support or pharmacological interventions in these patients.

Noninvasive mechanical ventilation is used in some patients with ARDS. When intubated and mechanically ventilated, ARDS patients should be ventilated with low tidal volumes. A plateau pressure <30 cmH₂O is recommended in all patients. It is suggested that a plateau pressure <15 cmH₂O should be considered safe. Patient with moderate and severe ARDS should receive higher levels of positive end-expiratory pressure (PEEP). Rescue therapies include prone position and neuromuscular blocking agents. Extracorporeal support for decapneisation and oxygenation should only be considered when lung-protective ventilation is no longer possible, or in cases of refractory hypoxaemia, respectively. Tracheotomy is only recommended when prolonged mechanical ventilation is expected.

Of all tested pharmacological interventions for ARDS, only treatment with steroids is considered to have benefit.

Proper identification of phenotypes, known to respond differently to specific interventions, is increasingly considered important for clinical trials of interventions for ARDS. Such phenotypes could be defined based on clinical parameters, such as the arterial oxygen tension/inspiratory oxygen fraction ratio, but biological marker profiles could be more promising.

Treatment of ARDS is mainly through the prevention of ventilation-induced lung injuryhttp://ow.ly/DeJC30hGWfi

Allergic asthma: an overview of metabolomic strategies leading to the identification of biomarkers in the field

Allergic asthma is a prominent disease especially during childhood. Indoor allergens, in general, and particularly house dust mites (HDM) are the most prevalent sensitizers associated with allergic asthma. Available data show that 65-130 million people are mite-sensitized world-wide and as many as 50% of these are asthmatic. In fact, sensitization to HDM in the first years of life can produce devastating effects on pulmonary function leading to asthmatic syndromes that can be fatal. To date, there has been considerable research into the pathological pathways and structural changes associated with allergic asthma. However, limitations related to the disease heterogeneity and a lack of knowledge into its pathophysiology have impeded the generation of valuable data needed to appropriately phenotype patients and, subsequently, treat this disease. Here, we report a systematic and integral analysis of the disease, from airway remodelling to the immune response taking place throughout the disease stages. We present an overview of metabolomics, the management of complex multifactorial diseases through the analysis of all possible metabolites in a biological sample, obtaining a global interpretation of biological systems. Special interest is placed on the challenges to obtain biological samples and the methodological aspects to acquire relevant information, focusing on the identification of novel biomarkers associated with specific phenotypes of allergic asthma. We also present an overview of the metabolites cited in the literature, which have been related to inflammation and immune response in asthma and other allergy-related diseases.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

How should treatment approaches differ depending on the severity of asthma?

INTRODUCTION

Asthma is nowadays regarded as a syndrome of various overlapping phenotypes with defined clinical characteristics, different underlying inflammatory mechanisms, identifiable genetic background, environmental risk factors and possible biomarkers. There are no doubts that due to the diversity of asthma, a 'one size fits all' management of the disease is no longer valid. Areas covered: Nowadays asthma management is based on the control of the disease, and the goals of asthma treatment are defined as good symptom control, decreased future risk of exacerbations, fixed airflow limitation, and side-effects of treatment. Alternative strategies for adjusting asthma treatment such as sputum or exhaled nitric oxide guided protocols have been evaluated and despite some effectiveness, are regarded as impractical in every-day clinical conditions. Further studies in the field of asthma phenotypes/endotypes and biomarkers are warranted with the main goal to define which of those possible subgroups will be useful in clinical practice in regards to the potential allocation of successful treatment. Expert commentary: Despite the availability of guidelines on the diagnosis and management of asthma, it seems that the disease is still not optimally controlled. Addressing unmet needs in every day care, improving education, adherence/compliance and inhalation technique may significantly improve asthma control across all severities of the disease.

A Transcriptome-driven Analysis of Epithelial Brushings and Bronchial Biopsies to Define Asthma Phenotypes in U-BIOPRED

RATIONALE

Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms.

OBJECTIVES

Using transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma.

METHODS

The transcriptome derived from bronchial biopsies and epithelial brushings of 107 subjects with moderate to severe asthma were annotated by gene set variation analysis using 42 gene signatures relevant to asthma, inflammation, and immune function. Topological data analysis of clinical and histologic data was performed to derive clusters, and the nearest shrunken centroid algorithm was used for signature refinement.

MEASUREMENTS AND MAIN RESULTS

Nine gene set variation analysis signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper cell type 2 cytokines and lack of corticosteroid response (group 1 and group 3). Group 1 had the highest submucosal eosinophils, as well as high fractional exhaled nitric oxide levels, exacerbation rates, and oral corticosteroid use, whereas group 3 patients showed the highest levels of sputum eosinophils and had a high body mass index. In contrast, group 2 and group 4 patients had an 86% and 64% probability, respectively, of having noneosinophilic inflammation. Using machine learning tools, we describe an inference scheme using the currently available inflammatory biomarkers sputum eosinophilia and fractional exhaled nitric oxide levels, along with oral corticosteroid use, that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity.

CONCLUSIONS

This analysis demonstrates the usefulness of a transcriptomics-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target T-helper cell type 2-mediated inflammation and/or corticosteroid insensitivity.

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

Identification of thyroxine-binding globulin as a candidate plasma marker of chronic obstructive pulmonary disease

Biomarkers for the management of chronic obstructive pulmonary disease (COPD) are limited. The aim of this study was to explore new plasma biomarkers in patients with COPD. Thyroxine-binding globulin (THBG) was initially identified by proteomics in a discovery panel and subsequently verified by enzyme-linked immunosorbent assay in another verification panel with a 1-year follow-up. THBG levels were elevated in patients with COPD (9.2±2.3 μg/mL) compared to those of the controls (6.6±2.0 μg/mL). Receiver operating characteristic curves suggested that THBG was able to slightly differentiate between patients with COPD and controls (area under the curve [AUC]: 0.814) and performed better if combined with fibrinogen (AUC: 0.858). THBG was more capable of distinguishing Global Initiative for Obstructive Lung Disease stages I–III and IV (AUC: 0.851) compared with fibrinogen (AUC 0.582). THBG levels were negatively associated with predicted percentage forced expiratory volume in 1 s and positively related to predicted percentage residual volume, RV/percentage total lung capacity, and percentage low-attenuation area. COPD patients with higher baseline THBG levels had a greater risk of acute exacerbation (AE) than those with lower THBG levels (P=0.014, by Kaplan–Meier curve; hazard ratio: 4.229, by Cox proportional hazards model). In summary, THBG is a potential plasma biomarker of COPD and can assist in the management of stable stage and AEs in COPD patients.

A European Respiratory Society technical standard: exhaled biomarkers in lung disease

Breath tests cover the fraction of nitric oxide in expired gas (F_eNO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for F_eNO, official recommendations for standardised procedures are more than 10 years old and there is none for exhaled VOCs and particles. The aim of this document is to provide technical standards and recommendations for sample collection and analytic approaches and to highlight future research priorities in the field. For EBC and F_eNO, new developments and advances in technology have been evaluated in the current document. This report is not intended to provide clinical guidance on disease diagnosis and management.Clinicians and researchers with expertise in exhaled biomarkers were invited to participate. Published studies regarding methodology of breath tests were selected, discussed and evaluated in a consensus-based manner by the Task Force members.Recommendations for standardisation of sampling, analysing and reporting of data and suggestions for research to cover gaps in the evidence have been created and summarised.Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice.

Persistent Bacterial Bronchitis: Time to Venture beyond the Umbrella

Chronic cough in children is common and frequently mismanaged. In the past, cough was diagnosed as asthma and inappropriate asthma therapies prescribed and escalated. It has been realized that persistent bacterial bronchitis (PBB) is a common cause of wet cough and responds to oral antibiotics. The initial definition comprised a history of chronic wet cough, positive bronchoalveolar (BAL) cultures for a respiratory pathogen and response to a 2-week course of oral amoxicillin-clavulanic acid. This is now termed PBB-micro; PBB-clinical eliminates the need for BAL. PBB-extended is PBB-micro or PBB-clinical but resolution necessitating 4 weeks of antibiotics; and recurrent PBB is >3 attacks of PBB-micro or-clinical/year. However, the airway has only a limited range of responses to chronic inflammation and infection, and neutrophilic airway disease is seen in many other conditions, such as cystic fibrosis and primary ciliary dyskinesia, both chronic suppurative lung disease endotypes, whose recognition has led to huge scientific and clinical advances. There is an urgent need to extend endotyping into PBB, especially PBB-recurrent. We need to move from associative studies and, in particular, deploy sophisticated modern -omics technologies and systems biology, rather as has been done in the context of asthma in U-BIOPRED. In summary, the use of the term PBB has done signal service in pointing us away from prescribing asthma therapies to children with infected airways, but we now need to move beyond a simple description to teasing out underlying endotypes.

Airway remodeling: Systems biology approach, from bench to bedside

Airway Remodeling, a patho-physiologic process, is considered as a key feature of chronic airway diseases. In recent years, our understanding of the complex diseases has increased significantly by the use of combined approaches, including systems biology, which may contribute to the development of personalized and predictive medicine approaches. Integrative analysis, along with the cooperation of clinicians, computer scientists, research scientists, and bench scientists, has become an important part of the experimental design and therapeutic strategies in the era of omics. The airway remodeling process is the result of the dysregulation of several signaling pathways that modulate the airway regeneration; therefore, high-throughput experiments and systems biology approach can help to understand this process better. The study reviews related literature and is consistent with the existing clinical evidence.

Systems Medicine for Lung Diseases: Phenotypes and Precision Medicine in Cancer, Infection, and Allergy

Lung diseases cause an enormous socioeconomic burden. Four of them are among the ten most important causes of deaths worldwide: Pneumonia has the highest death toll of all infectious diseases, lung cancer kills the most people of all malignant proliferative disorders, chronic obstructive pulmonary disease (COPD) ranks third in mortality among the chronic noncommunicable diseases, and tuberculosis is still one of the most important chronic infectious diseases. Despite all efforts, for example, by the World Health Organization and clinical and experimental researchers, these diseases are still highly prevalent and harmful. This is in part due to the specific organization of tissue homeostasis, architecture, and immunity of the lung. Recently, several consortia have formed and aim to bring together clinical and molecular data from big cohorts of patients with lung diseases with novel experimental setups, biostatistics, bioinformatics, and mathematical modeling. This "systems medicine" concept will help to match the different disease modalities with adequate therapeutic and possibly preventive strategies for individual patients in the sense of precision medicine.

Metabolomics Profiling for Obstructive Sleep Apnea and Simple Snorers

Few clinical studies have explored altered urinary metabolite levels in patients with obstructive sleep apnea (OSA). Thus, we applied a metabolomics approach to analyze urinary metabolites in three groups of participants: patients with polysomnography (PSG)-confirmed OSA, simple snorers (SS), and normal subjects. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and gas chromatography coupled with time-of-flight mass spectrometry were used. A total of 21 and 31 metabolites were differentially expressed in the SS and OSA groups, respectively. Patients with OSA had 18 metabolites different from those with SS. Of the 56 metabolites detected among the 3 groups, 24 were consistently higher or lower. A receiver operator curve analysis revealed that the combination of 4-hydroxypentenoic acid, arabinose, glycochenodeoxycholate-3-sulfate, isoleucine, serine, and xanthine produced a moderate diagnostic score with a sensitivity (specificity) of 75% (78%) for distinguishing OSA from those without OSA. The combination of 4-hydroxypentenoic acid, 5-dihydrotestosterone sulfate, serine, spermine, and xanthine distinguished OSA from SS with a sensitivity of 85% and specificity of 80%. Multiple metabolites and metabolic pathways associated with SS and OSA were identified using the metabolomics approach, and the altered metabolite signatures could potentially serve as an alternative diagnostic method to PSG.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes.

Clinical phenotypes of obstructive airway diseases in an outpatient population

BACKGROUND AND OBJECTIVES

Historically, obstructive airway diseases such as asthma and COPD are classified as different diseases. Although the definitions are clearly described, classification of patients into these traditional, clinical disease entity can be difficult. Recent evidence that there are complex, overlapping phenotypes of obstructive lung disease. Our aim was to capture clinical phenotypes of obstructive diseases through the use of cluster analysis in a representative patient population at a common Dutch pulmonary outpatient clinic. Clinical physiological and cellular/ molecular markers were used in the analysis.

METHODS

To carry out the cluster analysis, an imputed dataset was created from a random sample of 191 adult patients chosen from a pulmonary outpatient clinic. The selection criteria from the sample included patients with a doctor's diagnosis for asthma or COPD. Detailed assessment of patient pulmonary function, blood eosinophil counts, allergic sensitisation and smoking history was collected.

RESULTS

We observed four distinct clusters with different clinical characteristics of obstructive lung diseases. Cluster 1: patients with a history of extensive cigarette smoking, airway obstruction without signs of emphysema; cluster 2: patients with features of the emphysematous type of COPD; cluster 3: patients with characteristics of allergic asthma; cluster 4: patients with features suggesting an overlap syndrome of atopic asthma and COPD.

CONCLUSION

Four phenotypes of obstructive lung disease were identified amongst patients clinically labelled as asthma or COPD. These findings emphasize the concept that there are different phenotypes of obstructive lung diseases, including overlapping and complementary disease entities. These phenotypes of chronic airways disease can serve to tailor disease management.

Development of a CART Model to Predict the Synthesis of Cardiotoxic Daunorubicinol in Heart Tissue Samples From Donors With and Without Down Syndrome

Daunorubicin (DAUN) and doxorubicin (DOX) are used to treat a variety of cancers. The use of DAUN and DOX is hampered by the development of cardiotoxicity. Clinical evidence suggests that patients with leukemia and Down syndrome are at increased risk for anthracycline-related cardiotoxicity. Carbonyl reductases and aldo-keto reductases (AKRs) catalyze the reduction of DAUN and DOX into cardiotoxic C-13 alcohol metabolites. Anthracyclines also exert cardiotoxicity by triggering mitochondrial dysfunction. In recent studies, a collection of heart samples from donors with and without Down syndrome was used to investigate determinants for anthracycline-related cardiotoxicity including cardiac daunorubicin reductase activity (DA), carbonyl reductase/AKRs protein expression, mitochondrial DNA content (mtDNA), and AKR7A2 DNA methylation status. In this study, the available demographic, biochemical, genetic, and epigenetic data were integrated through classification and regression trees analysis with the aim of pinpointing the most relevant variables for the synthesis of cardiotoxic daunorubicinol (i.e., DA). Seventeen variables were considered as potential predictors. Leave-one-out-cross-validation was performed for model selection and to estimate the generalization error. The classification and regression trees analysis model and variable importance measures suggest that cardiac mtDNA content, mtDNA(4977) deletion frequency, and AKR7A2 protein content are the most important variables in determining DA.

Disease-specific dynamic biomarkers selected by integrating inflammatory mediators with clinical informatics in ARDS patients with severe pneumonia

Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome that occurs as a result of various risk factors, including either direct or indirect lung injury, and systemic inflammation triggered also by severe pneumonia (SP). SP-ARDS-associated morbidity and mortality remains high also due to the lack of disease-specific biomarkers. The present study aimed at identifying disease-specific biomarkers in SP or SP-ARDS by integrating proteomic profiles of inflammatory mediators with clinical informatics. Plasma was sampled from the healthy as controls or patients with SP infected with bacteria or infection-associated SP-ARDS on the day of admission, day 3, and day 7. About 15 or 52 cytokines showed significant difference between SP and SP-ARDS patients with controls or 13 between SP-ARDS with SP alone and controls, including bone morphogenetic protein-15 (BMP-15), chemokine (C-X-C motif) ligand 16 (CXCL16), chemokine (C-X-C motif) receptor 3 (CXCR3), interleukin-6 (IL-6), protein NOV homolog (NOV/CCN3), glypican 3, insulin-like growth factor binding protein 4 (IGFBP-4), IL-5, IL-5 R alpha, IL-22 BP, leptin, MIP-1d, and orexin B with a significant correlation with Digital Evaluation Score System (DESS) scores. ARDS patients with overexpressed IL-6, CXCL16, or IGFBP-4 had significantly longer hospital stay and higher incidence of secondary infection. We also found higher levels of those mediators were associated with poor survival rates in patients with lung cancer and involved in the process of the epithelial mesenchymal transition of alveolar epithelial cells. Our preliminary study suggested that integration of proteomic profiles with clinical informatics as part of clinical bioinformatics is important to validate and optimize disease-specific and disease-staged biomarkers.

Future Research Directions in Asthma. An NHLBI Working Group Report

Asthma is a common chronic disease without cure. Our understanding of asthma onset, pathobiology, classification, and management has evolved substantially over the past decade; however, significant asthma-related morbidity and excess healthcare use and costs persist. To address this important clinical condition, the NHLBI convened a group of extramural investigators for an Asthma Research Strategic Planning workshop on September 18-19, 2014, to accelerate discoveries and their translation to patients. The workshop focused on (1) in utero and early-life origins of asthma, (2) the use of phenotypes and endotypes to classify disease, (3) defining disease modification, (4) disease management, and (5) implementation research. This report summarizes the workshop and produces recommendations to guide future research in asthma.

Clinical implications of omics and systems medicine: focus on predictive and individualized treatment

Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide a brief introduction to systems medicine and discuss how it may contribute to the clinical implementation of individualized treatment, using clinically relevant examples.

Asthma phenotyping: a necessity for improved therapeutic precision and new targeted therapies

Asthma is a common heterogeneous disease with a complex pathophysiology that carries a significant mortality rate and high morbidity. Current therapies based on inhaled corticosteroids and long-acting β-agonists remain effective in a large proportion of patients with asthma, but ~10% (considered to have 'severe asthma') do not respond to these treatments even at high doses or with the use of oral corticosteroids. Analytical clustering methods have revealed phenotypes that include dependence on high-dose corticosteroid treatment, severe airflow obstruction and recurrent exacerbations associated with an allergic background and late onset of disease. One severe phenotype is eosinophilic inflammation-predominant asthma, with late-onset disease, rhinosinusitis, aspirin sensitivity and exacerbations. Blood and sputum eosinophilia have been used to distinguish patients with high Th2 inflammation and to predict therapeutic response to treatments targeted towards Th2-associated cytokines. New therapies in the form of humanized antibodies against Th2 targets, such as anti-IgE, anti-IL4Rα, anti-IL-5 and anti-IL-13 antibodies, have shown encouraging results in terms of reduction in exacerbations and improvement in airflow in patients with a 'Th2-high' expression profile and blood eosinophilia. Research efforts are now focusing on elucidating the phenotypes underlying the non-Th2-high (or Th2-low) group, which constitutes ~50% of severe asthma cases. There is an increasing need to use biomarkers to indicate the group of patients who will respond to a specifically targeted treatment. The use of improved tools to measure activity of disease, a better definition of severe asthma and the delineation of inflammatory pathways with omics analyses using computational tools, will lead to better-defined phenotypes for specific therapies.

A Practical Guide for Exploring Opportunities of Repurposing Drugs for CNS Diseases in Systems Biology

Systems biology has shown its potential in facilitating pathway-focused therapy development for central nervous system (CNS) diseases. An integrated network can be utilized to explore the multiple disease mechanisms and to discover repositioning opportunities. This review covers current therapeutic gaps for CNS diseases and the role of systems biology in pharmaceutical industry. We conclude with a Multiple Level Network Modeling (MLNM) example to illustrate the great potential of systems biology for CNS diseases. The system focuses on the benefit and practical applications in pathway centric therapy and drug repositioning.

Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness

Recent advances in genomics have led to the rapid and relatively inexpensive collection of patient molecular data including multiple types of omics data. The integration of these data with clinical measurements has the potential to impact on our understanding of the molecular basis of disease and on disease management. Systems medicine is an approach to understanding disease through an integration of large patient datasets. It offers the possibility for personalized strategies for healthcare through the development of a new taxonomy of disease. Advanced computing will be an important component in effectively implementing systems medicine. In this chapter we describe three computational challenges associated with systems medicine: disease subtype discovery using integrated datasets, obtaining a mechanistic understanding of disease, and the development of an informatics platform for the mining, analysis, and visualization of data emerging from translational medicine studies.

Childhood asthma prediction models: a systematic review

Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.

Allergen Immunotherapy (AIT): a prototype of Precision Medicine

Precision medicine is a medical model aiming to deliver customised healthcare - with medical decisions, practices, and/or products tailored to the individual patient informed but not directed by guidelines. Allergen immunotherapy has unique immunological rationale, since the approach is tailored to the specific IgE spectrum of an individual and modifies the natural course of the disease as it has a persistent efficacy after completion of treatment. In this perspective Allergen Immunotherapy - AIT has to be presently considered a prototype of Precision Medicine.

Precise information and biomarkers provided by systems medicine and network medicine will address the discovery of Allergen immunotherapy biomarkers for (i) identification of the causes, (ii) stratification of eligible patients for AIT and (iii) the assessment of AIT efficacy.

This area of medical technology is evolving rapidly and, compelemented by e-health, will change the way we practice medicine. It will help to monitor patients’ disease control and data for (i) patient stratification, (ii) clinical trials, (iii) monitoring the efficacy and safety of targeted therapies which are critical for reaching an appropriate reimbursement. Biomarkers associated with e-health combined with a clinical decision support system (CDSS) will change the scope of Allergen immunotherapy.

The cost/effectiveness of Allergen immunotherapy is a key issue for successful implementation. It should include the long-term benefits in the pharmaco-economic evaluation, since no other allergy treatment has this specific characteristic.

AIT is the prototype of current and future precision medicine.

The burden of severe asthma in childhood and adolescence: results from the paediatric U-BIOPRED cohorts

U-BIOPRED aims to characterise paediatric and adult severe asthma using conventional and innovative systems biology approaches. A total of 99 school-age children with severe asthma and 81 preschoolers with severe wheeze were compared with 49 school-age children with mild/moderate asthma and 53 preschoolers with mild/moderate wheeze in a cross-sectional study. Despite high-dose treatment, the severe cohorts had more severe exacerbations compared with the mild/moderate ones (annual medians: school-aged 3.0 versus 1.1, preschool 3.9 versus 1.8; p<0.001). Exhaled tobacco exposure was common in the severe wheeze cohort. Almost all participants in each cohort were atopic and had a normal body mass index. Asthma-related quality of life, as assessed by the Paediatric Asthma Quality of Life Questionnaire (PAQLQ) and the Paediatric Asthma Caregiver's Quality of Life Questionnaire (PACQLQ), was worse in the severe cohorts (mean±se school-age PAQLQ: 4.77±0.15 versus 5.80±0.19; preschool PACQLQ: 4.27±0.18 versus 6.04±0.18; both p≤0.001); however, mild/moderate cohorts also had significant morbidity. Impaired quality of life was associated with poor control and airway obstruction. Otherwise, the severe and mild/moderate cohorts were clinically very similar. Children with severe preschool wheeze or severe asthma are usually atopic and have impaired quality of life that is associated with poor control and airflow limitation: a very different phenotype from adult severe asthma. In-depth phenotyping of these children, integrating clinical data with high-dimensional biomarkers, may help to improve and tailor their clinical management.

MACVIA-ARIA Sentinel NetworK for allergic rhinitis (MASK-rhinitis): the new generation guideline implementation

Several unmet needs have been identified in allergic rhinitis: identification of the time of onset of the pollen season, optimal control of rhinitis and comorbidities, patient stratification, multidisciplinary team for integrated care pathways, innovation in clinical trials and, above all, patient empowerment. MASK-rhinitis (MACVIA-ARIA Sentinel NetworK for allergic rhinitis) is a simple system centred around the patient which was devised to fill many of these gaps using Information and Communications Technology (ICT) tools and a clinical decision support system (CDSS) based on the most widely used guideline in allergic rhinitis and its asthma comorbidity (ARIA 2015 revision). It is one of the implementation systems of Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). Three tools are used for the electronic monitoring of allergic diseases: a cell phone-based daily visual analogue scale (VAS) assessment of disease control, CARAT (Control of Allergic Rhinitis and Asthma Test) and e-Allergy screening (premedical system of early diagnosis of allergy and asthma based on online tools). These tools are combined with a clinical decision support system (CDSS) and are available in many languages. An e-CRF and an e-learning tool complete MASK. MASK is flexible and other tools can be added. It appears to be an advanced, global and integrated ICT answer for many unmet needs in allergic diseases which will improve policies and standards.

Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort

U-BIOPRED is a European Union consortium of 20 academic institutions, 11 pharmaceutical companies and six patient organisations with the objective of improving the understanding of asthma disease mechanisms using a systems biology approach.This cross-sectional assessment of adults with severe asthma, mild/moderate asthma and healthy controls from 11 European countries consisted of analyses of patient-reported outcomes, lung function, blood and airway inflammatory measurements.Patients with severe asthma (nonsmokers, n=311; smokers/ex-smokers, n=110) had more symptoms and exacerbations compared to patients with mild/moderate disease (n=88) (2.5 exacerbations versus 0.4 in the preceding 12 months; p<0.001), with worse quality of life, and higher levels of anxiety and depression. They also had a higher incidence of nasal polyps and gastro-oesophageal reflux with lower lung function. Sputum eosinophil count was higher in severe asthma compared to mild/moderate asthma (median count 2.99% versus 1.05%; p=0.004) despite treatment with higher doses of inhaled and/or oral corticosteroids.Consistent with other severe asthma cohorts, U-BIOPRED is characterised by poor symptom control, increased comorbidity and airway inflammation, despite high levels of treatment. It is well suited to identify asthma phenotypes using the array of "omic" datasets that are at the core of this systems medicine approach.

Clinical phenotypes of asthma should link up with disease mechanisms

PURPOSE OF REVIEW

Asthma is a common disease which presents in various clinical forms and levels of severity. The current 'one size fits all' approach to treatment is suboptimal. Using unbiased cluster analysis has identified several asthma phenotypes. Understanding the underlying mechanisms driving these clusters may lead to better patient-orientated medicines.

RECENT FINDINGS

Clustering was initially performed on clinical features only, but the addition of biomarkers that characterize sputum and blood cellular profiles has enabled the prediction of responses to targeted therapies. Clusters of severe asthma include those on high-dose corticosteroid treatment associated with severe airflow obstruction and those with discordance between symptoms and sputum eosinophilia. Sputum eosinophilia can predict therapeutic responses to T-helper type 2 cytokine blockade. Further molecular phenotyping or endotyping of asthma will be necessary to determine new treatment strategies. Low T-helper type 2 expression may be predictive of poor therapeutic response to inhaled corticosteroids, but much less is known about this type of asthma.

SUMMARY

Phenotype-driven treatment of asthma will be further boosted by the integration of genetic, transcriptomic and proteomic technologies to defining distinct severe asthma phenotypes and biomarkers of therapeutic responses. This will lead towards stratified medicine for asthma.

Phenotyping asthma, rhinitis and eczema in MeDALL population-based birth cohorts: an allergic comorbidity cluster

BACKGROUND

Asthma, rhinitis and eczema often co-occur in children, but their interrelationships at the population level have been poorly addressed. We assessed co-occurrence of childhood asthma, rhinitis and eczema using unsupervised statistical techniques.

METHODS

We included 17 209 children at 4 years and 14 585 at 8 years from seven European population-based birth cohorts (MeDALL project). At each age period, children were grouped, using partitioning cluster analysis, according to the distribution of 23 variables covering symptoms 'ever' and 'in the last 12 months', doctor diagnosis, age of onset and treatments of asthma, rhinitis and eczema; immunoglobulin E sensitization; weight; and height. We tested the sensitivity of our estimates to subject and variable selections, and to different statistical approaches, including latent class analysis and self-organizing maps.

RESULTS

Two groups were identified as the optimal way to cluster the data at both age periods and in all sensitivity analyses. The first (reference) group at 4 and 8 years (including 70% and 79% of children, respectively) was characterized by a low prevalence of symptoms and sensitization, whereas the second (symptomatic) group exhibited more frequent symptoms and sensitization. Ninety-nine percentage of children with comorbidities (co-occurrence of asthma, rhinitis and/or eczema) were included in the symptomatic group at both ages. The children's characteristics in both groups were consistent in all sensitivity analyses.

CONCLUSION

At 4 and 8 years, at the population level, asthma, rhinitis and eczema can be classified together as an allergic comorbidity cluster. Future research including time-repeated assessments and biological data will help understanding the interrelationships between these diseases.

Defining phenotypes in asthma: a step towards personalized medicine

Asthma is a common disease with a complex pathophysiology. It can present in various clinical forms and with different levels of severity. Unbiased cluster analytic methods have unravelled several phenotypes in cohorts representative of the whole spectrum of severity. Clusters of severe asthma include those on high-dose corticosteroid treatment, often with both inhaled and oral treatment, usually associated with severe airflow obstruction. Phenotypes with concordance between symptoms and sputum eosinophilia have been reported, including an eosinophilic inflammation-predominant group with few symptoms and late-onset disease who have a high prevalence of rhinosinusitis, aspirin sensitivity, and exacerbations. Sputum eosinophilia is also a biomarker that can predict therapeutic responses to antibody-based treatments to block the effects of the T-helper (Th)-2 cytokine, interleukin (IL)-5. Low Th2-expression has been predictive of poor therapeutic response to inhaled corticosteroid therapy. Current asthma schedules emphasise a step-up approach to treating asthma in relation to increasing severity, but, in more severe disease, phenotyping or endotyping of asthma will be necessary to determine new treatment strategies as severe asthma is recognized as being a particularly heterogeneous disease. Much less is known about 'non-eosinophilic' asthma. Phenotypic characterisation of corticosteroid insensitivity and chronic airflow obstruction of severe asthma is also needed. Phenotype-driven treatment of asthma will be further boosted by the advent of transcriptomic and proteomic technologies, with the application of systems biology or medicine approaches to defining phenotypes and biomarkers of disease and therapeutic response. This will pave the way towards personalized medicine and healthcare for asthma.

Innate and adaptive T cells in asthmatic patients: Relationship to severity and disease mechanisms

Background

Asthma is a chronic inflammatory disease involving diverse cells and mediators whose interconnectivity and relationships to asthma severity are unclear.

Objective

We performed a comprehensive assessment of T_H17 cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, other T-cell subsets, and granulocyte mediators in asthmatic patients.

Methods

Sixty patients with mild-to-severe asthma and 24 control subjects underwent detailed clinical assessment and provided induced sputum, endobronchial biopsy, bronchoalveolar lavage, and blood samples. Adaptive and invariant T-cell subsets, cytokines, mast cells, and basophil mediators were analyzed.

Results

Significant heterogeneity of T-cell phenotypes was observed, with levels of IL-13–secreting T cells and type 2 cytokines increased at some, but not all, asthma severities. T_H17 cells and γδ-17 cells, proposed drivers of neutrophilic inflammation, were not strongly associated with asthma, even in severe neutrophilic forms. MAIT cell frequencies were strikingly reduced in both blood and lung tissue in relation to corticosteroid therapy and vitamin D levels, especially in patients with severe asthma in whom bronchoalveolar lavage regulatory T-cell numbers were also reduced. Bayesian network analysis identified complex relationships between pathobiologic and clinical parameters. Topological data analysis identified 6 novel clusters that are associated with diverse underlying disease mechanisms, with increased mast cell mediator levels in patients with severe asthma both in its atopic (type 2 cytokine–high) and nonatopic forms.

Conclusion

The evidence for a role for T_H17 cells in patients with severe asthma is limited. Severe asthma is associated with a striking deficiency of MAIT cells and high mast cell mediator levels. This study provides proof of concept for disease mechanistic networks in asthmatic patients with clusters that could inform the development of new therapies.