Clinical phenotypes of asthma should link up with disease mechanisms

Transcriptomic evaluation of skin tape-strips in children with allergic asthma uncovers epidermal barrier dysfunction and asthma-associated biomarkers abnormalities

INTRODUCTION

Tape-strips, a minimally invasive method validated for the evaluation of several skin diseases, may help identify asthma-specific biomarkers in the skin of children with allergic asthma.

METHODS

Skin tape-strips were obtained and analyzed with RNA-Seq from children with moderate allergic asthma (MAA) (n = 11, mean age 7.00; SD = 1.67), severe allergic asthma (SAA) (n = 9, mean age 9.11; SD = 2.37), and healthy controls (HCs) (n = 12, mean age 7.36; SD = 2.03). Differentially expressed genes (DEGs) were identified by fold change ≥2 with a false discovery rate <0.05. Transcriptomic biomarkers were analyzed for their accuracy in distinguishing asthma from HCs, their relationships with asthma-related outcomes (exacerbation rate, lung function-FEV1, IOS-R5-20, and lung inflammation-FeNO), and their links to skin (barrier and immune response) and lung (remodeling, metabolism, aging) pathogenetic pathways.

RESULTS

RNA-Seq captured 1113 in MAA and 2117 DEGs in SAA. Epidermal transcriptomic biomarkers for terminal differentiation (FLG/filaggrin), cell adhesion (CDH19, JAM2), lipid biosynthesis/metabolism (ACOT2, LOXL2) were significantly downregulated. Gene set variation analysis revealed enrichment of Th1/IFNγ pathways (p < .01). MAA and SAA shared downregulation of G-protein-coupled receptor (OR4A16, TAS1R3), upregulation of TGF-β/ErbB signaling-related (ACVR1B, EGFR, ID1/2), and upregulation of mitochondrial-related (HIGD2A, VDAC3, NDUFB9) genes. Skin transcriptomic biomarkers correlated with the annualized exacerbation rate and with lung function parameters. A two-gene classifier (TSSC4-FAM212B) was able to differentiate asthma from HCs with 100% accuracy.

CONCLUSION

Tape-strips detected epithelial barrier and asthma-associated signatures in normal-appearing skin from children with allergic asthma and may serve as an alternative to invasive approaches for evaluating asthma endotypes.

Inhibiting ATG5 mediated autophagy to regulate endoplasmic reticulum stress and CD4+ T lymphocyte differentiation: Mechanisms of acupuncture's effects on asthma

OBJECTIVE

Asthma is characterized by airway hyperresponsiveness(AHR), inflammation and remodeling. Autophagy and endoplasmic reticulum stress(ERS) are dysregulated in asthma, and ATG5 has attracted wide attentions a representative gene of autophagy. Previous evidence shows that acupuncture may treat asthma by regulating the immune environment.However,the precise mechanism involved in acupuncture's effects on asthma is unclear. Thus, we investigated the inner-relationships of acupuncture and ATG5-mediated autophagy, ERS and CD4+ T lymphocyte differentiation in asthma.

METHODS

Ovalbumin (OVA)-sensitized and challenged ATG5+/- and ATG5-/-mice with asthma were treated by acupuncture at Dazhui(GV14),Feishu(BL13) and Zusanli(ST36),and sacrificed the next day.Then blood and bronchoalveolar lavage fluid (BALF)samples were collected to determine inflammatory cell counts and cytokine levels. Lung tissue samples were obtained for histological examination, and the spleen was harvested for flow cytometry.

RESULTS

Compared with the untreated group, acupuncture decreased BALF inflammatory cell counts and AHR in OVA-induced mice.Acupuncture decreased autophagy-related protein and mRNA (ATG5,Beclin-1,p62 and LC3B)amounts and ERS-related protein (p-PERK, p-IRE-1,Grp78, and ATF6)levels as well as autophagosome formation in lung tissue, concomitant with increased IFN-γ and decreased IL-4, IL-17 and TGF-β amounts in BALF.Consistently, the imbalance of CD4+ T lymphocyte subsets(Th1/Th2 and Treg/Th17) was also corrected by acupuncture.Meanwhile, AHR and inflammation were decreased in ATG5-/- mice compared with ATG+/-animals,without affecting the therapeutic effect of acupuncture.

CONCLUSION

Acupuncture reduces airway inflammation and AHR in asthma by inhibiting ATG5-mediated autophagy to regulate endoplasmic reticulum stress and CD4+T lymphocyte differentiation.

Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children

Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV₁ and MEF₅₀), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N = 58) and 3 (N = 138) had better treatment outcomes compared to clusters 2 and 4 and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6 + yrs). Clusters 2 (N = 87) and 4 (N = 64) had poorer treatment success, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2), level of systemic inflammation (highest hsCRP for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes.

Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. The most predictive variables for asthma control are related to asthma severity and the total IgE, which were also predictive for FENO-based outcomes. MEF50-related treatment outcomes were better predicted than the FEV1-based response, and one of the best predictive variables for this response was hsCRP, emphasizing the involvement of the distal airways in childhood asthma. Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. T2-high asthma seemed to respond best to the anti-inflammatory treatment. The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.

The application of proteomics in the diagnosis and treatment of bronchial asthma

Bronchial asthma is a common chronic inflammatory disease of the airways. Although its pathogenic mechanism remains unknown, it is influenced by both genetic and environmental factors. The emergence and application of proteomic technologies can help to facilitate analysis of the changes in transcription factors, inflammatory mediators, chemokines, cytokines, and cell apoptosis-and proliferation-related proteins in the pathological processes of asthma. Proteomic technologies can unearth prospects and theoretical bases for improved understanding of the biological mechanism of asthma and effective identification of diagnostic and therapeutic targets.

Differences in the Clinical Characteristics of Early- and Late-Onset Asthma in Elderly Patients

Differences between early-onset and late-onset asthma in elderly subjects have not been comprehensively described in China. We conducted a cross-sectional study to determine the phenotypic differences between early-onset asthma (EOA) and late-onset asthma (LOA) in elderly patients. We collected clinical and physiological data from 176 elderly patients with asthma. Participants were divided into two groups: EOA group and LOA group. Demographics, comorbidities, inflammatory parameters, lung function, severity, asthma control, and medication use among EOA and LOA elderly patients were compared. Elderly subjects with EOA had more atopic disease, a stronger positive family history of asthma, higher IgE, and exhaled nitric oxide levels as compared to those with LOA. In contrast, elderly subjects with LOA had lower lung function and more marked fixed airflow obstruction (FAO). Elderly subjects with LOA had a higher incidence of chronic obstructive pulmonary disease (COPD). No differences were observed in age, gender, BMI, history of smoking, severity, and asthma control between the two groups. Both similarities and differences exist between elderly subjects with EOA and those with LOA in China. Further work is required to determine the pathophysiological, clinical, and therapeutic implications for different asthma phenotypes in elderly subjects.

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.

Precision medicine for the discovery of treatable mechanisms in severe asthma

Although the complex disease of asthma has been defined as being heterogeneous, the extent of its endophenotypes remains unclear. The pharmacological approach to initiating treatment has, until recently, been based on disease control and severity. The introduction of antibody therapies targeting the Type 2 inflammation pathway for patients with severe asthma has resulted in the recognition of an allergic and an eosinophilic phenotype, which are not mutually exclusive. Concomitantly, molecular phenotyping based on a transcriptomic analysis of bronchial epithelial and sputum cells has identified a Type 2 high inflammation cluster characterized by eosinophilia and recurrent exacerbations, as well as Type 2 low clusters linked with IL-6 trans-signalling, interferon pathways, inflammasome activation and mitochondrial oxidative phosphorylation pathways. Systems biology approaches are establishing the links between these pathways or mechanisms, and clinical and physiologic features. Validation of these pathways contributes to defining endotypes and treatable mechanisms. Precision medicine approaches are necessary to link treatable mechanisms with treatable traits and biomarkers derived from clinical, physiologic and inflammatory features of clinical phenotypes. The deep molecular phenotyping of airway samples along with noninvasive biomarkers linked to bioinformatic and machine learning techniques will enable the rapid detection of molecular mechanisms that transgresses beyond the concept of treatable traits.

Phenotypes of atopic dermatitis identified by cluster analysis in early childhood

Atopic dermatitis is a chronic, relapsing, inflammatory skin disease that usually appears in early childhood and develops into a heterogeneous disease during childhood. The clinical course and treatment for atopic dermatitis can differ according to its phenotype and/or endotype. This study aimed to identify clinical phenotypes of atopic dermatitis in early childhood. Data were obtained from 572 children under 3 years of age with atopic dermatitis. Cluster analysis applied to 11 variables, and we identified four clusters of atopic dermatitis. Children in cluster A (n = 141) had early-onset atopic dermatitis with high blood eosinophil counts, serum total immunoglobulin E and rates of sensitization to food allergens. Children in cluster B (n = 218) had early-onset atopic dermatitis with low blood eosinophil counts, serum total immunoglobulin E and rates of sensitization to both food and inhalant allergens. Children in cluster C (n = 53) had early-onset atopic dermatitis with high C-reactive protein levels and white blood cell counts. Children in cluster D (n = 160) had middle-onset atopic dermatitis with high serum total immunoglobulin E and rates of sensitization to inhalant allergens. Cluster A had the highest Scoring for Atopic Dermatitis and transepidermal water loss values. Age at onset, age at diagnosis, white blood cell count, eosinophil count, C-reactive protein and serum total immunoglobulin E level were the strongest predictors of cluster assignment. Analysis of these six variables alone resulted in correct classification of 95.5% of the subjects. These results support the heterogeneity of atopic dermatitis, even in early childhood.

Precision medicine in asthma: linking phenotypes to targeted treatments

PURPOSE OF REVIEW

Asthma is a heterogeneous disease consisting of different phenotypes that are driven by different mechanistic pathways. The purpose of this review is to emphasize the important role of precision medicine in asthma management.

RECENT FINDINGS

Despite asthma heterogeneity, the approach to management has been on the basis of disease severity, with the most severe patients reserved for the maximum treatments with corticosteroids and bronchodilators. At the severe end, the recent availability of biologic therapies in the form of anti-IgE (omalizumab) and anti-IL5 therapies (mepolizumab and reslizumab) has driven the adaptation of precision medicine. These therapies are reserved for severe asthma with defined either allergic or eosinophilic background, respectively.

SUMMARY

Unbiased definition of phenotypes or endotypes (which are phenotypes defined by mechanisms) is an important step towards the use of precision medicine in asthma. Although T2-high asthma has been defined with targets becoming available for treating allergic or eosinophilic asthma, the definition of non-T2 phenotypes remains a priority. Precision medicine is also dependent on the definition of biomarkers that can help differentiate between these phenotypes and pinpoint patients suitable for specific-targeted therapies. Thus, precision medicine links phenotypes (endotypes) to targeted treatments for better outcomes.

Personalised medicine in asthma: time for action: Number 1 in the Series "Personalised medicine in respiratory diseases" Edited by Renaud Louis and Nicolas Roche

Asthma is a heterogeneous disease comprising several phenotypes driven by different pathways. To define these phenotypes or endotypes (phenotypes defined by mechanisms), an unbiased approach to clustering of various omics platforms will yield molecular phenotypes from which composite biomarkers can be obtained. Biomarkers can help differentiate between these phenotypes and pinpoint patients suitable for specific targeted therapies - the basis for personalised medicine. Biomarkers need to be linked to point-of-care biomarkers that may be measured readily in exhaled breath, blood or urine. The potential for using mobile healthcare approaches will help patient enpowerment, an essential tool for personalised medicine. Personalised medicine in asthma is not far off - it is already here, but we need more tools and implements to carry it out for the benefit of our patients.

Near-fatal asthma: a heterogeneous clinical entity

PURPOSE OF REVIEW

The aims of the present review were to describe the heterogeneous nature of near-fatal asthma (NFA) and to summarize the distinctive phenotypes identified in this subtype of asthma.

RECENT FINDINGS

Clinical, physiological, and histological studies have shown a large number of triggers, pathological mechanisms, and risk factors associated with NFA. Based on the demographic and clinical characteristics of the patients, the circumstances surrounding the asthma exacerbation and some distinctive features of the disease, several clinical profiles of asthma patients with NFA have been described. Recent data show new associations between some gene expression patterns and fatal asthma, as well as with some biological markers related to inflammatory or immunologic mechanisms in the airways. Also, the use of statistical methods, such as cluster analysis, allowed identifying and confirming different phenotypes of life-threatening asthma patients.

SUMMARY

NFA is a heterogeneous clinical entity, in which different patients' clinical profiles may coexist [e.g. rapid-onset NFA, NFA in patients with dyspnea hypoperception or sensitized to certain pneumoallergens (Alternaria alternata, soybean), NFA related to the menstrual cycle, brittle asthma]. Knowledge of these phenotypes as well as adequate and specific management strategies can reduce morbidity and mortality in patients suffering from NFA.

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.

Clinical management of severe therapy-resistant asthma

INTRODUCTION

Severe therapy-resistant asthma is an area where there has been recent advances in understanding that is percolating into improvements in management. Areas covered: This review covers the recent definition and approach to the diagnosis of severe asthma and its differentiation from difficult-to-treat asthma. The recent advances in phenotyping severe asthma and in ensuing changes in management approaches together with the introduction of new therapies are covered from a review of the recent literature. Expert commentary: After ascertaining the diagnosis of severe asthma, patients need to be treated adequately with existing therapies. The management approach to severe asthma now comprises of a phenotyping step for the definition of either an allergic or eosinophilic severe asthma for which targeted therapies are currently available. This will lead to a precision medicine approach to the management of severe asthma.

Polymorphism 4G/5G of the plasminogen activator inhibitor 1 gene as a risk factor for the development of allergic rhinitis symptoms in patients with asthma

Plasminogen activator inhibitor-1 (PAI-1) is a glycoprotein which has a role in tissue remodelling after inflammatory processes. The objective is to investigate the frequency of PAI-1 gene polymorphism (4G/5G) in patients with a lung ventilation dysfunction in asthma and allergic rhinitis. Genomic DNA was isolated and genotypes of polymorphism of PAI-1 4G/5G and ABO were determined using the methods of RT-PCR and PCR-SSP. Study group includes 145 adult patients diagnosed with chronic asthma, with all clinically relevant parameters and the laboratory markers of pO₂, IgE and eosinophils in sputum and nasal swab. In the processing of data, appropriate statistical tests (Kolmogorov-Smirnov test, median, interquartile ranges, χ ² and Mann-Whitney U tests) were used. Patients with symptoms of allergic rhinitis were significantly younger and had an almost four time higher levels of IgE (P = 0.001), higher pO₂ (P = 0.002) and PEF (P = 0.036), compared to those who do not have these symptoms. Genotype PAI 4G/4G is significantly more common in patients with allergic rhinitis (28.1% vs. 16.1%; P = 0.017) compared to the genotype 5G/5G. Carriers of the genotype 4G/5G also have a borderline statistical significance. There were no statistically significant difference in the incidence of allergic rhinitis in the carriers of any ABO genotypes. The frequency of PAI genotype 4G/4G is significantly more common in patients with allergic rhinitis. The results suggest that the carriers of at least one 4G allele are at a higher risk for developing symptoms of allergic rhinitis in asthma.

Patient Characteristics and Individualization of Biologic Therapy

Progress in the understanding of disease processes has provided additional therapeutic targets, best exemplified by the increasing role of biologics in the clinical armamentarium. This article provides a focused review of current treatment paradigms and pathophysiology for asthma, atopic dermatitis, urticaria, as well as C1 inhibitor deficiency. It elucidates the populations in which biologics were studied for the aforementioned disease states, emphasizing characteristics to consider when selecting therapy. It is important to correctly estimate patient outcome before starting therapy based on cost analysis. Treatment decisions need to be guided by appropriate patient stratification based on each individual's underlying phenotype.

Syk Regulates Neutrophilic Airway Hyper-Responsiveness in a Chronic Mouse Model of Allergic Airways Inflammation

Background

Asthma is a chronic inflammatory disease characterized by airways hyper-responsiveness (AHR), reversible airway obstruction, and airway inflammation and remodeling. We previously showed that Syk modulates methacholine-induced airways contractility in naïve mice and in mice with allergic airways inflammation. We hypothesize that Syk plays a role in the pathogenesis of AHR; this was evaluated in a chronic 8-week mouse model of house dust mite (HDM)-induced allergic airways inflammation.

Methods

We used the Syk^flox/flox//rosa26CreER^T2 conditional Syk knock-out mice to assess the role of Syk prior to HDM exposure, and treated HDM-sensitized mice with the Syk inhibitor, GSK143, to evaluate its role in established allergic airways inflammation. Respiratory mechanics and methacholine (MCh)-responsiveness were assessed using the flexiVent^® system. Lungs underwent bronchoalveolar lavage to isolate inflammatory cells or were frozen for determination of gene expression in tissues.

Results

MCh-induced AHR was observed following HDM sensitization in the Syk-intact (Syk^flox/flox) and vehicle-treated BALB/c mice. MCh responsiveness was reduced to control levels in HDM-sensitized Syk^del/del mice and in BALB/c and Syk^flox/flox mice treated with GSK143. Both Syk^del/del and GSK143-treated mice mounted appropriate immune responses to HDM, with HDM-specific IgE levels that were comparable to Syk^flox/flox and vehicle-treated BALB/c mice. HDM-induced increases in bronchoalveolar lavage cell counts were attenuated in both Syk^del/del and GSK143-treated mice, due primarily to decreased neutrophil recruitment. Gene expression analysis of lung tissues revealed that HDM-induced expression of IL-17 and CXCL-1 was significantly attenuated in both Syk^del/del and GSK143-treated mice.

Conclusion

Syk inhibitors may play a role in the management of neutrophilic asthma.

Effect of exposure to an Asian dust storm on fractional exhaled nitric oxide in adult asthma patients in Western Japan

BACKGROUND

Epidemiological investigations indicate that an Asian dust storm (ADS) can aggravate respiratory disorders. However, the effects of ADS on airway inflammation remain unclear. The aim of this study was to investigate the association of exposure to ADS with airway inflammation.

METHODS

The subjects were 33 adult patients with asthma who measured daily peak flow expiratory (PEF) from March to May 2012. Fractional exhaled nitric oxide (FeNO) was measured before and after ADS.

RESULTS

The FeNO values were 13.8±13.7 ppb before the ADS and 20.3±19.0 ppb after the ADS, with no significant difference. There was also no significant association of PEF with ADS exposure. However, the increase of FeNO after ADS exposure was proportional to the decrease of PEF (R=-0.78, P<0.0001).

CONCLUSION

These results suggest that airway inflammation aggravated by ADS exposure may induce a decrease in pulmonary function in some adult patients with asthma.

Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Breathomics in the setting of asthma and chronic obstructive pulmonary disease

Exhaled breath contains thousands of volatile organic compounds that reflect the metabolic process occurring in the host both locally in the airways and systemically. They also arise from the environment and airway microbiome. Comprehensive analysis of breath volatile organic compounds (breathomics) provides opportunities for noninvasive biomarker discovery and novel mechanistic insights. Applications in patients with obstructive lung diseases, such as asthma and chronic obstructive pulmonary disease, include not only diagnostics (especially in children and other challenging diagnostic areas) but also identification of clinical treatable traits, such as airway eosinophilia and risk of infection/exacerbation, that are not specific to diagnostic labels. Although many aspects of breath sampling and analysis are challenging, proof-of-concept studies with mass spectrometry and electronic nose technologies have provided independent studies with moderate-to-good diagnostic and phenotypic accuracies. The present review evaluates the data obtained by using breathomics in (1) predicting the inception of asthma or chronic obstructive pulmonary disease, (2) inflammatory phenotyping, (3) exacerbation prediction, and (4) treatment stratification. The current findings merit the current efforts of large multicenter studies using standardized sampling, shared analytic methods, and databases, including external validation cohorts. This will position this noninvasive technology in the clinical assessment and monitoring of chronic airways diseases.

Patient stratification and the unmet need in asthma

Asthma is often described as an inflammatory disease of the lungs and in most patients symptomatic treatment with bronchodilators or inhaled corticosteroids is sufficient to control disease. Unfortunately there are a proportion of patients who fail to achieve control despite treatment with the best current treatment. These severe asthma patients have been considered a homogeneous group of patients that represent the unmet therapeutic need in asthma. Many novel therapies have been tested in unselected asthma patients and the effects have often been disappointing, particularly for the highly specific monoclonal antibody-based drugs such as anti-IL-13 and anti-IL-5. More recently, it has become clear that asthma is a syndrome with many different disease drivers. Clinical trials of anti-IL-13 and anti-IL-5 have focused on biomarker-defined patient groups and these trials have driven the clinical progression of these drugs. Work on asthma phenotyping indicates that there is a group of asthma patients where T helper cell type 2 (Th2) cytokines and inflammation predominate and these type 2 high (T2-high) patients can be defined by biomarkers and response to therapies targeting this type of immunity, including anti-IL-5 and anti-IL-13. However, there is still a subset of T2-low patients that do not respond to these new therapies. This T2-low group will represent the new unmet medical need now that the T2-high-targeting therapies have made it to the market. This review will examine the current thinking on patient stratification in asthma and the identification of the T2-high subset. It will also look at the T2-low patients and examine what may be the drivers of disease in these patients.

United airway disease: current perspectives

Upper and lower airways are considered a unified morphological and functional unit, and the connection existing between them has been observed for many years, both in health and in disease. There is strong epidemiologic, pathophysiologic, and clinical evidence supporting an integrated view of rhinitis and asthma: united airway disease in the present review. The term “united airway disease” is opportune, because rhinitis and asthma are chronic inflammatory diseases of the upper and lower airways, which can be induced by allergic or nonallergic reproducible mechanisms, and present several phenotypes. Management of rhinitis and asthma must be jointly carried out, leading to better control of both diseases, and the lessons of the Allergic Rhinitis and Its Impact on Asthma initiative cannot be forgotten.

Asthma phenotypes and endotypes: a personalized approach to treatment

PURPOSE OF REVIEW

Asthma is quite common and is better described as a syndrome with a heterogeneous presentation than as a single disease. Although most individuals can be effectively managed using a guideline-directed approach to care, those with the most severe illness may benefit from a more targeted therapy. The review describes our current understanding of how asthma phenotypes (observable characteristics) and endotypes (specific biologic mechanisms) can be employed to gain insight into asthma pathobiology and personalized therapy.

RECENT FINDINGS

Our understanding of the heterogeneity of asthma is increasing. The concept of asthma phenotype has become more complex, incorporating both clinical and biologic features. Several asthma endotypes (e.g., allergic bronchopulmonary mycosis, aspirin-exacerbated respiratory disease, severe late-onset hypereosinophilic asthma, etc.) have been proposed, but further research is needed to delineate specific mechanisms underlying asthma pathogenesis. Several biologic therapies targeting certain phenotypes are in development and are expected to broaden our armamentarium for treatment of severe asthma.

SUMMARY

Asthma is a heterogeneous condition with diverse characteristics and biologic mechanisms. Severe asthma is associated with significant morbidity and even mortality and represents a major unmet need. Stratification of asthma subtypes into phenotypes and endotypes should move the field forward in terms of more effective and personalized treatment.

Molecular phenotyping and biomarker development: are we on our way towards targeted therapy for severe asthma?

Although different phenotypes of severe asthma can be identified, all are characterized by common symptoms. Due to their heterogeneity, they exhibit differences in pathogenesis, etiology and clinical responses to therapeutic approaches. The identification of distinct molecular phenotypes to define severe asthmatic patients will allow us to better understand the pathophysiology of the disease and thus to more precisely target the treatment for each patient. To achieve this goal, a systematic search for new, reliable and stable biomarkers specific for each phenotype is essential. This review focuses on the current known molecular phenotypes of severe asthma and highlights the need for biomarkers that could (either alone or in combination) be predictive of the treatment outcome.