Oropharyngeal Microbiota Clusters in Children with Asthma or Wheeze Associate with Allergy, Blood Transcriptomic Immune Pathways, and Exacerbation Risk.

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis ß-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus, Veillonella, Rothia, and Haemophilus. The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV1% predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-ß (transforming growth factor-ß) (highest in the Veillonella cluster) and Wnt/ß-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

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.

Transgelin: a new gene involved in LDL endocytosis identified by a genome-wide CRISPR-Cas9 screen.

A significant proportion of patients with elevated LDL and a clinical presentation of familial hypercholesterolemia do not carry known genetic mutations associated with hypercholesterolemia, such as defects in the LDL receptor. To identify new genes involved in the cellular uptake of LDL, we developed a novel whole-genome clustered regularly interspaced short palindromic repeat-Cas9 KO screen in HepG2 cells. We identified transgelin (TAGLN), an actin-binding protein, as a potentially new gene involved in LDL endocytosis. In silico validation demonstrated that genetically predicted differences in expression of TAGLN in human populations were significantly associated with elevated plasma lipids (triglycerides, total cholesterol, and LDL-C) in the Global Lipids Genetics Consortium and lipid-related phenotypes in the UK Biobank. In biochemical studies, TAGLN-KO HepG2 cells showed a reduction in cellular LDL uptake, as measured by flow cytometry. In confocal microscopy imaging, TAGLN-KO cells had disrupted actin filaments as well as an accumulation of LDL receptor on their surface because of decreased receptor internalization. Furthermore, TAGLN-KO cells exhibited a reduction in total and free cholesterol content, activation of SREBP2, and a compensatory increase in cholesterol biosynthesis. TAGLN deficiency also disrupted the uptake of VLDL and transferrin, other known cargoes for receptors that depend upon clathrin-mediated endocytosis. Our data suggest that TAGLN is a novel factor involved in the actin-dependent phase of clathrin-mediated endocytosis of LDL. The identification of novel genes involved in the endocytic uptake of LDL may improve the diagnosis of hypercholesterolemia and provide future therapeutic targets for the prevention of cardiovascular disease.

Heterogeneity within and between physician-diagnosed asthma and/or COPD: NOVELTY cohort.

BACKGROUND: Studies of asthma and chronic obstructive pulmonary disease (COPD) typically focus on these diagnoses separately, limiting understanding of disease mechanisms and treatment options. NOVELTY is a global, 3-year, prospective observational study of patients with asthma and/or COPD from real-world clinical practice. We investigated heterogeneity and overlap by diagnosis and severity in this cohort. METHODS: Patients with physician-assigned asthma, COPD or both (asthma+COPD) were enrolled, and stratified by diagnosis and severity. Baseline characteristics were reported descriptively by physician-assigned diagnosis and/or severity. Factors associated with physician-assessed severity were evaluated using ordinal logistic regression analysis. RESULTS: Of 11 243 patients, 5940 (52.8%) had physician-assigned asthma, 1396 (12.4%) had asthma+COPD and 3907 (34.8%) had COPD; almost half were from primary care. Symptoms, health-related quality of life and spirometry showed substantial heterogeneity and overlap between asthma, asthma+COPD and COPD, with 23%, 62% and 64% of patients, respectively, having a ratio of post-bronchodilator forced expiratory volume in 1 s to forced vital capacity below the lower limit of normal. Symptoms and exacerbations increased with greater physician-assessed severity and were higher in asthma+COPD. However, 24.3% with mild asthma and 20.4% with mild COPD had experienced ≥1 exacerbation in the past 12 months. Medication records suggested both under-treatment and over-treatment relative to severity. Blood eosinophil counts varied little across diagnosis and severity groups, but blood neutrophil counts increased with severity across all diagnoses. CONCLUSION: This analysis demonstrates marked heterogeneity within, and overlap between, physician-assigned diagnosis and severity groups in patients with asthma and/or COPD. Current diagnostic and severity classifications in clinical practice poorly differentiate between clinical phenotypes that may have specific risks and treatment implications.

Stratification of asthma phenotypes by airway proteomic signatures.

BACKGROUND: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. OBJECTIVE: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. METHODS: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. RESULTS: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms. CONCLUSION: This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies.

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.

Genetic variation in FCER1A predicts peginterferon alfa-2a-induced hepatitis B surface antigen clearance in East Asian patients with chronic hepatitis B.

In a multicentre, genome-wide association study to identify host genetic factors associated with treatment response in adult chronic hepatitis B patients, genotype data were obtained by microarray analysis from 1669 patients who received peginterferon alfa-2a for ≥ 24 weeks with/without a nucleos(t)ide analog. Treatment response was assessed at least 24 weeks post-treatment, using serological and/or virological endpoints. Thirty-six single-marker analyses and a gene-by-gene analysis were conducted. No single nucleotide polymorphisms (SNPs) achieved genome-wide significance (P < 5 × 10-8 ) in single-marker analyses, but suggestive associations (P < 1 × 10-5 ) were identified for 116 SNPs. In gene-by-gene analyses, one gene, FCER1A (rs7549785), reached genome-wide significance (P = 2.65 × 10-8 ) in East Asian patients for hepatitis B surface antigen (HBsAg) clearance, with a moderate effect size (odds ratio = 4.74). Eleven of 44 carriers (25%) of the A allele at rs7549785 achieved HBsAg clearance compared with 69/1051 (7%) noncarriers. FCER1A encodes the alpha subunit of the immunoglobulin E receptor. In a post hoc analysis of a homogenous patient subset, the strongest intragenic association was for rs7712322 (POLR3G, P = 7.21 × 10-7 ). POLR3G encodes the G subunit of the polymerase (RNA) III enzyme, involved in sensing and limiting infection by intracellular bacteria and DNA viruses, and as a DNA sensor in innate immune responses. FCER1A (rs7549785) and possibly POLR3G (rs7712322) are shown to be associated with peginterferon alfa-2a response in adult patients with chronic hepatitis B. Independent confirmation of these findings is warranted (clinicaltrials.gov number NCT01855997).

Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma.

BACKGROUND: Sputum analysis in asthmatic patients is used to define airway inflammatory processes and might guide therapy. OBJECTIVE: We sought to determine differential gene and protein expression in sputum samples from patients with severe asthma (SA) compared with nonsmoking patients with mild/moderate asthma. METHODS: Induced sputum was obtained from nonsmoking patients with SA, smokers/ex-smokers with severe asthma, nonsmoking patients with mild/moderate asthma (MMAs), and healthy nonsmoking control subjects. Differential cell counts, microarray analysis of cell pellets, and SOMAscan analysis of sputum analytes were performed. CRID3 was used to inhibit the inflammasome in a mouse model of SA. RESULTS: Eosinophilic and mixed neutrophilic/eosinophilic inflammation were more prevalent in patients with SA compared with MMAs. Forty-two genes probes were upregulated (>2-fold) in nonsmoking patients with severe asthma compared with MMAs, including IL-1 receptor (IL-1R) family and nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain containing 3 (NRLP3) inflammasome members (false discovery rate < 0.05). The inflammasome proteins nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing 1 (NLRP1), NLRP3, and nucleotide-binding oligomerization domain (NOD)-like receptor C4 (NLRC4) were associated with neutrophilic asthma and with sputum IL-1ß protein levels, whereas eosinophilic asthma was associated with an IL-13-induced TH2 signature and IL-1 receptor-like 1 (IL1RL1) mRNA expression. These differences were sputum specific because no activation of NLRP3 or enrichment of IL-1R family genes in bronchial brushings or biopsy specimens in patients with SA was observed. Expression of NLRP3 and of the IL-1R family genes was validated in the Airway Disease Endotyping for Personalized Therapeutics cohort. Inflammasome inhibition using CRID3 prevented airway hyperresponsiveness and airway inflammation (both neutrophilia and eosinophilia) in a mouse model of severe allergic asthma. CONCLUSION: IL1RL1 gene expression is associated with eosinophilic SA, whereas NLRP3 inflammasome expression is highest in patients with neutrophilic SA. TH2-driven eosinophilic inflammation and neutrophil-associated inflammasome activation might represent interacting pathways in patients with SA.

Pathway discovery using transcriptomic profiles in adult-onset severe asthma.

BACKGROUND: Adult-onset severe asthma is characterized by highly symptomatic disease despite high-intensity asthma treatments. Understanding of the underlying pathways of this heterogeneous disease is needed for the development of targeted treatments. Gene set variation analysis is a statistical technique used to identify gene profiles in heterogeneous samples. OBJECTIVE: We sought to identify gene profiles associated with adult-onset severe asthma. METHODS: This was a cross-sectional, observational study in which adult patients with adult-onset of asthma (defined as starting at age ≥18 years) as compared with childhood-onset severe asthma (<18 years) were selected from the U-BIOPRED cohort. Gene expression was assessed on the total RNA of induced sputum (n = 83), nasal brushings (n = 41), and endobronchial brushings (n = 65) and biopsies (n = 47) (Affymetrix HT HG-U133+ PM). Gene set variation analysis was used to identify differentially enriched predefined gene signatures of leukocyte lineage, inflammatory and induced lung injury pathways. RESULTS: Significant differentially enriched gene signatures in patients with adult-onset as compared with childhood-onset severe asthma were identified in nasal brushings (5 signatures), sputum (3 signatures), and endobronchial brushings (6 signatures). Signatures associated with eosinophilic airway inflammation, mast cells, and group 3 innate lymphoid cells were more enriched in adult-onset severe asthma, whereas signatures associated with induced lung injury were less enriched in adult-onset severe asthma. CONCLUSIONS: Adult-onset severe asthma is characterized by inflammatory pathways involving eosinophils, mast cells, and group 3 innate lymphoid cells. These pathways could represent useful targets for the treatment of adult-onset severe asthma.

Large-Scale Label-Free Quantitative Mapping of the Sputum Proteome.

Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMSE applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The "core" sputum proteome (proteins detected in ≥40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMSE is influenced by several factors, with some proteins being measured in all participants' samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.

Lipid phenotyping of lung epithelial lining fluid in healthy human volunteers.

BACKGROUND: Lung epithelial lining fluid (ELF)-sampled through sputum induction-is a medium rich in cells, proteins and lipids. However, despite its key role in maintaining lung function, homeostasis and defences, the composition and biology of ELF, especially in respect of lipids, remain incompletely understood. OBJECTIVES: To characterise the induced sputum lipidome of healthy adult individuals, and to examine associations between different ELF lipid phenotypes and the demographic characteristics within the study cohort. METHODS: Induced sputum samples were obtained from 41 healthy non-smoking adults, and their lipid compositions analysed using a combination of untargeted shotgun and liquid chromatography mass spectrometry methods. Topological data analysis (TDA) was used to group subjects with comparable sputum lipidomes in order to identify distinct ELF phenotypes. RESULTS: The induced sputum lipidome was diverse, comprising a range of different molecular classes, including at least 75 glycerophospholipids, 13 sphingolipids, 5 sterol lipids and 12 neutral glycerolipids. TDA identified two distinct phenotypes differentiated by a higher total lipid content and specific enrichments of diacyl-glycerophosphocholines, -inositols and -glycerols in one group, with enrichments of sterols, glycolipids and sphingolipids in the other. Subjects presenting the lipid-rich ELF phenotype also had significantly higher BMI, but did not differ in respect of other demographic characteristics such as age or gender. CONCLUSIONS: We provide the first evidence that the ELF lipidome varies significantly between healthy individuals and propose that such differences are related to weight status, highlighting the potential impact of (over)nutrition on lung lipid metabolism.

U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics.

BACKGROUND: Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalized management approach can be provided. OBJECTIVES: We stratified patients with moderate-to-severe asthma based on clinicophysiologic parameters and performed an omics analysis of sputum. METHODS: Partition-around-medoids clustering was applied to a training set of 266 asthmatic participants from the European Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) adult cohort using 8 prespecified clinic-physiologic variables. This was repeated in a separate validation set of 152 asthmatic patients. The clusters were compared based on sputum proteomics and transcriptomics data. RESULTS: Four reproducible and stable clusters of asthmatic patients were identified. The training set cluster T1 consists of patients with well-controlled moderate-to-severe asthma, whereas cluster T2 is a group of patients with late-onset severe asthma with a history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of nonsmokers. Cluster T4 is predominantly composed of obese female patients with uncontrolled severe asthma with increased exacerbations but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters (T2, T3, and T4) had higher sputum eosinophilia than cluster T1, with no differences in sputum neutrophil counts and exhaled nitric oxide and serum IgE levels. CONCLUSION: Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.

Transcriptomic gene signatures associated with persistent airflow limitation in patients with severe asthma.

A proportion of severe asthma patients suffers from persistent airflow limitation (PAL), often associated with more symptoms and exacerbations. Little is known about the underlying mechanisms. Here, our aim was to discover unexplored potential mechanisms using Gene Set Variation Analysis (GSVA), a sensitive technique that can detect underlying pathways in heterogeneous samples.Severe asthma patients from the U-BIOPRED cohort with PAL (post-bronchodilator forced expiratory volume in 1 s/forced vital capacity ratio below the lower limit of normal) were compared with those without PAL. Gene expression was assessed on the total RNA of sputum cells, nasal brushings, and endobronchial brushings and biopsies. GSVA was applied to identify differentially enriched predefined gene signatures based on all available gene expression publications and data on airways disease.Differentially enriched gene signatures were identified in nasal brushings (n=1), sputum (n=9), bronchial brushings (n=1) and bronchial biopsies (n=4) that were associated with response to inhaled steroids, eosinophils, interleukin-13, interferon-α, specific CD4+ T-cells and airway remodelling.PAL in severe asthma has distinguishable underlying gene networks that are associated with treatment, inflammatory pathways and airway remodelling. These findings point towards targets for the therapy of PAL in severe asthma.

Severe asthma exists despite suppressed tissue inflammation: findings of the U-BIOPRED study.

The U-BIOPRED study is a multicentre European study aimed at a better understanding of severe asthma. It included three steroid-treated adult asthma groups (severe nonsmokers (SAn group), severe current/ex-smokers (SAs/ex group) and those with mild-moderate disease (MMA group)) and healthy controls (HC group). The aim of this cross-sectional, bronchoscopy substudy was to compare bronchial immunopathology between these groups.In 158 participants, bronchial biopsies and bronchial epithelial brushings were collected for immunopathologic and transcriptomic analysis. Immunohistochemical analysis of glycol methacrylate resin-embedded biopsies showed there were more mast cells in submucosa of the HC group (33.6 mm-2) compared with both severe asthma groups (SAn: 17.4 mm-2, p<0.001; SAs/ex: 22.2 mm-2, p=0.01) and with the MMA group (21.2 mm-2, p=0.01). The number of CD4+ lymphocytes was decreased in the SAs/ex group (4.7 mm-2) compared with the SAn (11.6 mm-2, p=0.002), MMA (10.1 mm-2, p=0.008) and HC (10.6 mm-2, p<0.001) groups. No other differences were observed.Affymetrix microarray analysis identified seven probe sets in the bronchial brushing samples that had a positive relationship with submucosal eosinophils. These mapped to COX-2 (cyclo-oxygenase-2), ADAM-7 (disintegrin and metalloproteinase domain-containing protein 7), SLCO1A2 (solute carrier organic anion transporter family member 1A2), TMEFF2 (transmembrane protein with epidermal growth factor like and two follistatin like domains 2) and TRPM-1 (transient receptor potential cation channel subfamily M member 1); the remaining two are unnamed.We conclude that in nonsmoking and smoking patients on currently recommended therapy, severe asthma exists despite suppressed tissue inflammation within the proximal airway wall.

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.

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.

Genetic markers of bevacizumab-induced hypertension.

PURPOSE: There are currently no validated biomarkers predicting bevacizumab treatment outcome or toxicity. We combined biomarker data from six phase III trials of bevacizumab to assess whether genetic variation in vascular endothelial growth factor-A (VEGF-A) pathway or hypertension-related genes are associated with bevacizumab-induced hypertension. EXPERIMENTAL DESIGN: Germline DNA was available from 1,631 patients receiving bevacizumab-containing therapy for advanced solid tumors. Overall, 194 white patients had grade 1-4 bevacizumab-induced hypertension. In total, 236 single nucleotide polymorphisms (SNPs) located in VEGF-A, VEGF-A receptors (FLT1 and KDR), and other genes were selected using a SNP tagging approach and genotyped. A logistic regression on individual patient data was performed after adjustment for cancer type and five other covariates. RESULTS: Ten SNPs were associated with bevacizumab-induced hypertension (P ≤ 0.05), but none surpassed the threshold adjusted for multiple testing (P < 0.0002). The most significant VEGF-A pathway SNP was rs1680695 in EGLN3 [allelic odds ratio (OR) 1.50 [95 % confidence interval (Cl) 1.09-2.07], P = 0.012]. Two additional SNPs, rs4444903 in EGF and rs2305949 in KDR, were associated with hypertension (allelic OR 1.57 [95 % CI 1.17-2.11], P = 0.0025; allelic OR 0.62 [95 % CI 0.42-0.93], P = 0.020, respectively) and closely linked to nearby functional variants. Consistent with previous reports, rs11064560 in WNK1 was also associated with bevacizumab-induced hypertension (OR 1.41 [95 % CI 1.04-1.92], P = 0.028). CONCLUSIONS: The genes described in this large genetic analysis using pooled datasets warrant further functional investigation regarding their role in mediating bevacizumab-induced hypertension.

Genetic variability of VEGF pathway genes in six randomized phase III trials assessing the addition of bevacizumab to standard therapy.

BACKGROUND: Despite extensive translational research, no validated biomarkers predictive of bevacizumab treatment outcome have been identified. METHODS: We performed a meta-analysis of individual patient data from six randomized phase III trials in colorectal, pancreatic, lung, renal, breast, and gastric cancer to explore the potential relationships between 195 common genetic variants in the vascular endothelial growth factor (VEGF) pathway and bevacizumab treatment outcome. RESULTS: The analysis included 1,402 patients (716 bevacizumab-treated and 686 placebo-treated). Twenty variants were associated (P < 0.05) with progression-free survival (PFS) in bevacizumab-treated patients. Of these, 4 variants in EPAS1 survived correction for multiple testing (q < 0.05). Genotype-by-treatment interaction tests revealed that, across these 20 variants, 3 variants in VEGF-C (rs12510099), EPAS1 (rs4953344), and IL8RA (rs2234671) were potentially predictive (P < 0.05), but not resistant to multiple testing (q > 0.05). A weak genotype-by-treatment interaction effect was also observed for rs699946 in VEGF-A, whereas Bayesian genewise analysis revealed that genetic variability in VHL was associated with PFS in the bevacizumab arm (q < 0.05). Variants in VEGF-A, EPAS1, and VHL were located in expression quantitative loci derived from lymphoblastoid cell lines, indicating that they affect the expression levels of their respective gene. CONCLUSIONS: This large genetic analysis suggests that variants in VEGF-A, EPAS1, IL8RA, VHL, and VEGF-C have potential value in predicting bevacizumab treatment outcome across tumor types. Although these associations did not survive correction for multiple testing in a genotype-by-interaction analysis, they are among the strongest predictive effects reported to date for genetic variants and bevacizumab efficacy.