Plasma proteins elevated in severe asthma despite oral steroid use and unrelated to Type-2 inflammation

Rationale Asthma phenotyping requires novel biomarker discovery. Objectives To identify plasma biomarkers associated with asthma phenotypes by application of a new proteomic panel to samples from two well-characterised cohorts of severe (SA) and mild-to-moderate (MMA) asthmatics, COPD subjects and healthy controls (HCs). Methods An antibody-based array targeting 177 proteins predominantly involved in pathways relevant to inflammation, lipid metabolism, signal transduction and extracellular matrix was applied to plasma from 525 asthmatics and HCs in the U-BIOPRED cohort, and 142 subjects with asthma and COPD from the validation cohort BIOAIR. Effects of oral corticosteroids (OCS) were determined by a 2-week, placebo-controlled OCS trial in BIOAIR, and confirmed by relation to objective OCS measures in U-BIOPRED. Results In U-BIOPRED, 110 proteins were significantly different, mostly elevated, in SA compared to MMA and HCs. 10 proteins were elevated in SA versus MMA in both U-BIOPRED and BIOAIR (alpha-1-antichymotrypsin, apolipoprotein-E, complement component 9, complement factor I, macrophage inflammatory protein-3, interleukin-6, sphingomyelin phosphodiesterase 3, TNF receptor superfamily member 11a, transforming growth factor-β and glutathione S-transferase). OCS treatment decreased most proteins, yet differences between SA and MMA remained following correction for OCS use. Consensus clustering of U-BIOPRED protein data yielded six clusters associated with asthma control, quality of life, blood neutrophils, high-sensitivity C-reactive protein and body mass index, but not Type-2 inflammatory biomarkers. The mast cell specific enzyme carboxypeptidase A3 was one major contributor to cluster differentiation. Conclusions The plasma proteomic panel revealed previously unexplored yet potentially useful Type-2-independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA.

Subjects with severe asthma had been under follow-up by a respiratory specialist for at least 6 months prior to enrolment in the study. During this time, investigations to exclude other diagnoses were performed and steps taken to optimise asthma control (treatment of co-morbidities, assessment of adherence, and reduction in allergen exposure). Non-smokers with severe asthma (SAn) were defined according to GINA guidelines [1] and/or frequent exacerbations (more than 2 per year) despite highdose ICS (ICS ≥1000 µg fluticasone propionate/day or equivalent dose, or a doubling of the dose of maintenance ICS for at least three days or requiring hospitalisation), with or without OCS, plus at least one other controller medication. They had been non-smokers for at least the past 12 months with a pack-year history of ≤5. Smoker and ex-smokers with severe asthma (SAs/ex) were defined as SAn except being either current smokers or former smokers with a pack-year history of >5.
Non-smokers with mild-to-moderate asthma (MMA) were defined by GINA guidelines [1] and had controlled or partially controlled asthma symptoms whilst receiving a dose of less than 500 µg fluticasone propionate/day or equivalent. They had been non-smokers for at least the past 12 months with a pack-year history of ≤5.
Healthy non-smoking controls (HC) had no history of asthma, wheeze or other chronic respiratory disease. They had a pre-bronchodilator FEV1 of ≥80% predicted and were non-smokers for at least the past 12 months with pack-year history of ≤5.
Exclusion criteria included a history of recreational drug use, allergy which contraindicated participation, or a recent history of incapacitating psychiatric disorders. Female subjects who were pregnant or breast feeding (or up to 6 weeks post-partum or cessation of breast feeding) were also excluded. No participation within 3 months after the first dose using a new molecular entity, drug or invasive procedure in another study. In addition, subjects were to have no history of or current evidence of upper or lower respiratory infection or symptoms within 2 weeks of baseline assessment (in which case assessments were deferred).

Study design
In this prospective cohort study, all subjects were identified and recruited locally and attended a screening visit when eligibility for the study was confirmed. All subjects underwent a baseline visit (up to 28 days later) at which study data and biological samples were collected.

BIOAIR
Asthma and COPD were diagnosed by pulmonary specialists according to published criteria [2][3][4][5]. The diagnosis of asthma had to be confirmed by at least one of the four criteria for reversible airway obstruction as documented during the last 5 years before the study or at screening visit, namely: 1) an increase in FEV1 ≥9% of predicted (or improvement of 200 ml) after administration of four puffs of 100 µg salbutamol dose-aerosol inhaled via a spacer, or after additional inhalation of four puffs of 20 µg ipratropium bromide administered through a large volume spacer; 2) mean diurnal variation in PEF of ≥15% on ≥4 days/week for at least 2 weeks, as calculated by the following equation: (highest PEF-lowest PEF)/mean PEF; 3) an increase in FEV1 of at least 400 ml after a course of prednisolone 0.5 mg/kg/day for 14 days; 4) in patients with a FEV1 ≥70% predicted, demonstrated bronchial hyperresponsiveness to histamine, methacholine, isocapnic hyperventilation, exercise or other indirect challenges (according to established local methods).
Subjects in the severe asthma group had been under specialist treatment for at least one year and had experienced at least one exacerbation in the past year. Exacerbations were defined either by the initiation of a course of OCS therapy in those individuals on regular ICS treatment, or for those on regular OCS therapy, a significant temporary increase in their dose of oral steroids for an acute deterioration in their disease control. Severe asthmatics had to require continuous treatment with high doses of ICS (at least 1600 µg/day budesonide or beclomethasone, 800 µg/day fluticasone or equivalent). For those taking oral steroids, the inhaled dose of steroids had to be at least 800 µg/day budesonide or beclomethasone, 400 µg/day fluticasone or equivalent. In addition, the subjects had to require continuous treatment with LABA or oral theophylline, as documented for at least one year.
Non-smokers with mild-to-moderate asthma (MMA) had stable disease and received daily treatment with a maximum of 800 µg/day budesonide or beclomethasone, 500 µg/day fluticasone or equivalent. MMA subjects used SABA as needed but did not require treatment with LABA and had had no exacerbations, nor hospitalisations in the past year.
Subjects recruited to the COPD group had a FEV1/FVC (Forced Vital Capacity) ratio of less than 0.7, and a post-bronchodilator FEV1 value between 30 and 80% of predicted, that increased by less than 9% (or 200 ml) after inhalation of bronchodilator. They were required to have been treated with ICS in the range of 800-1600 µg/day (beclomethasone, budesonide or equivalent) for at least three months prior to the study. A smoking history of more than 15 pack years, either as a current or ex-smoker, was required in the COPD group, whereas asthma patients who had smoked for more than 5 pack years were excluded.
Subjects were excluded if they were pregnant, had a history of alcohol or illicit drug abuse, had other acute or chronic pulmonary disorders or had clinically relevant psychiatric disease. They were not allowed to be receiving immunosuppressants other than corticosteroids, or undergoing immunotherapy. Subjects receiving chronic oxygen therapy were also excluded.

Study design
Subjects were screened during a half-day visit in one of the participating clinics following which all subjects underwent a 4-week treatment optimisation period. Medication details are shown below: The optimisation period was followed by a 2-week double-blind placebo-controlled steroid intervention, consisting of a standard course of oral prednisolone of 0.5 mg/kg of body weight/day or placebo added to regular treatment. This was followed by an identical open OCS treatment of the placebo group only.

Designing a protein panel for plasma profiling
We designed a panel for protein profiling based on knowledge within the consortium, searches in databases (Ensembl, Genotator, the Human Protein Atlas, Ingenuity Pathway Analysis, Intomics, MetaCore) and the literature. The selection covered proteins reported or proposed to be dysregulated in asthma or chronic obstructive pulmonary disease (COPD) and/or being expressed in blood, lung or during inflammation, and resulted in 177 proteins (Table S2). Biological processes reflected by these proteins were the immune response, lipid mediator pathways, signal transduction, extracellular matrix, energy/metabolic, coagulation and complement system. These proteins were targeted by 377 antibodies, the majority affinity-purified polyclonal antibodies from the Human Protein Atlas project (HPA, www.proteinatlas.org) [6] in addition to anti-CHI3L1 (AF2599 and MAB25991, R&D Systems, Minneapolis, MN, USA) and anti-HPGDS (MAB6487, R&D Systems). The antibody set included at least two and up to five antibodies per protein target for 125 of the proteins (71% of the full protein panel), see Table S3. These so-called paired antibodies were raised against the same or different amino acid sequences of the protein (ranging from partial to full protein sequence coverage and an average length of 80 amino acids).

Protein profiling using suspension bead arrays
Protein profiling in plasma was performed using antibody suspension bead arrays. The in-house developed array-based affinity proteomics method enables, in combination with the HPA antibodies, a dual dimensional multiplexing capacity for the analysis of 384 samples in parallel on 384 proteins. To create the bead array, antibodies were separately and covalently coupled to magnetic color-coded beads (MagPlex, Luminex Corp., Austin, TX, USA) before being combined into a multiplex suspension as previously described [7,8].
Layouts of 96-well microtiter plates were designed for each of the two cohorts, U-BIOPRED and BIOAIR, separately. This design controlled for a balanced distribution of disease groups and controls (U-BIOPRED cohort: mild-to-moderate asthma [MMA], non-smokers with severe asthma [SAn], smokers or ex-smokers with severe asthma [SAs/ex], healthy non-smoking controls [HC]; BIOAIR cohort: MMA, SAn, COPD); intervention group (applicable for BIOAIR); gender and age across the multiple plates. In BIOAIR, where sampling was done at multiple time points, all samples from each subject were analysed on the same plate. The samples were then randomized within each plate and distributed using a liquid handling device (EVO150, Tecan Group, Männedorf, Switzerland). In addition, sample-free PBS buffer in duplicates and technical replicates constituting a pool of all samples within a cohort were included on each plate as negative controls and for quality assessment, respectively.
Plasma samples were diluted in PBS and the protein content directly labelled with biotin. Labelled samples were diluted in assay buffer, heat treated at 56°C before being combined with antibody-coupled beads in 384-well microtiter plates. Incubation took place overnight. Measurements were performed using FlexMAP3D instruments (Luminex corp.), in parallel where applicable, reporting relative fluorescent intensity values.

Data pre-processing
Outliers were identified and removed based on robust principal component analysis (PCA) and the R package "rrcov" [9]. This resulted in exclusion of samples from 6 subjects (4 SAn, 1 MMA, 1 HC) from the U-BIOPRED dataset and samples from all timepoints of 2 COPD subjects in the BIOAIR dataset. These samples are excluded from the numbers presented and not included in Table 1 and Table S1 or in the statistical analysis. The raw intensity values were processed to minimize batch effects using the R package "MDimNormn" and function "normn_MA" [10], such that for each antibody the geometric mean of each plate was adjusted to match the geometric mean of the geometric mean values of multiple plates, thereby resulting in equal means for each plate.

Statistical analysis
Statistical analysis and visualization were performed in R [11,12].

Univariate statistics
Nonparametric Kruskal-Wallis and Wilcoxon rank-sum tests were used for multiple group and pairwise group comparisons of continuous variables, respectively. Wilcoxon signed-rank tests were used in paired comparisons. For all proteins, P values were adjusted for multiple testing using the Benjamini and Hochberg [13] method and controlling the false discovery rate (FDR) at 5%.

Consensus cluster analysis
Consensus clustering is a method for class discovery where the clustering results are validated by a resampling scheme that simulates perturbations of the data, thereby allowing for assessment of the robustness of the clusters [14]. From the U-BIOPRED data, a reduced set of variables (n=139 antibodies) from 434 subjects with asthma (263 SAn, 95 SAs/ex, and 76 MMA) was selected for consensus cluster analysis on log2 transformed and z-scored intensity signals of each antibody. Variables were selected if significantly different (adjusted P <0.05) in the multiple group comparison in U-BIOPRED (Table S4). Consensus cluster analysis was performed using the 'ConsensusClusterPlus' package in R [15]. The Euclidean distance measure was used to describe similarity between subjects and partitioning around the medoids algorithm used for clustering. The clustering was repeated 1000 times, removing randomly 10% of the subjects at each iteration. Models of two to ten clusters were generated and cluster stability evaluated by two methods: 1) localizing the cluster model having the lowest proportion of ambiguously clustered subjects [16] which is demonstrated by the cluster model number having the longest flat line in the cumulative distribution function plot; and 2) the lowest deviation from ideal stability also based on the cumulative distribution function [17]. Evaluation of cluster models identified ten clusters to have slightly better numerical stability than six (Table S8, Figure S2). However, all clusters in the six-cluster model demonstrated greater consistency compared to all clusters in the ten-cluster model ( Figure S2E) and was therefore selected for clinical and biochemical characterization in more detail. In an exploratory analysis, Kruskal-Wallis and Chi-squared tests were used to identify clinical and haematological variables that were significantly different between clusters.

Classification analysis with variable selection and random forest
To identify which of the 139 variables used in the consensus clustering were relevant for classification of the six clusters, machine learning was applied with feature (variable) selection based on a random forest classification algorithm.
In the pre-processing steps, there were no variables that needed to be removed due to zero or near-zero variance in their values and no variables with missing data.
Feature selection was performed using the Boruta algorithm (R package 'Boruta', all parameters set to default except number of trees ntree=1000, maximal number of iterations maxRuns=1000) [18]. Boruta is a so-called all-relevant variable method, meaning that it identifies all variables that are relevant for the classification. Boruta assesses the relevance by comparing the importance of the original variables with the importance of random permutations of the original variables. The importance of each variable is provided by a specified machine learning method. Here, the default method Random forest (R package 'ranger') was applied. To alleviate the problem of class imbalance, we adjusted the weights for the classes in the splitting rule of the random-forest algorithm according to the frequency of the classes. In short, Boruta works by first making a copy of each variable and randomly mixes the values of these to create "shadow variables". This is followed by training a Random forest model on the extended dataset and extracting the importance measures (ranger normalized permutation importance). Original variables with a higher importance than the maximum importance seen for any of the shadow variables are recorded as a hit. The process is then repeated for a set number of times and a statistical test is used to test if a variable has significantly higher importance score than the shadow variables. P values were adjusted with the Bonferroni method and P values <0.01 considered significant. Variables are defined as confirmed important, unimportant or tentatively important (i.e. unresolved). Definition of abbreviations: ACQ-7 = asthma control questionnaire-average of 7; AQLQ = asthma quality of life questionnaire-average; BMI = body mass index; COPD = chronic obstructive pulmonary disease; FeNO = fraction of exhaled nitric oxide; FEV 1 = forced expiratory volume in 1 s; FVC = forced vital capacity; HC = healthy non-smoking controls; hsCRP = high-sensitivity C-reactive protein; MMA = non-smokers with mild-to-moderate asthma; OCS = oral corticosteroids; SAn = non-smokers with severe asthma; SAs/ex = smokers or ex-smokers with severe asthma; SGRQ = St. George's Respiratory Questionnaire score; WBC = white blood cells.

SUPPLEMENTARY TABLES AND FIGURES
colony stimulating factor 2 ENSG00000164400  ) as well as directionality, indicated with (+) or (-), meaning higher or lower in the first group mentioned, are also reported.    ) as well as directionality, indicated with (+) or (-), meaning higher or lower in the first group mentioned, are also reported.  Table S6. Proteins associated with OCS use in non-smoking severe asthmatics in the U-BIOPRED cohort. Proteins (n=20) and the corresponding antibodies (n=21) identified to show significantly different plasma levels in SAn subjects that did take OCS vs. those that did not take OCS and where urinary OCS markers were also not detected (adjusted Wilcoxon rank-sum test*). Table S8. Identification of optimal number of clusters in consensus clustering of the U-BIOPRED cohort. To find the optimal number of clusters, cluster models with two and up to ten clusters were evaluated based on cluster stability using two different methods, "Proportion of ambiguous clustering" and "Deviation from ideal stability". Marked in grey are the two lowest values within the respective methods, indicating that a model of six or ten clusters has the best stability.    . Note that no proteins showed differential levels between SAn vs. COPD. COPD = chronic obstructive pulmonary disease; HC = healthy non-smoking controls; MMA = non-smokers with mild-tomoderate asthma; SAn = non-smokers with severe asthma; SAs/ex = smokers or ex-smokers with severe asthma. Figure S2. Consensus clustering in U-BIOPRED. Clustering was applied to U-BIOPRED subjects with asthma (n=434) using the input of protein profiles from 139 antibodies (identified to show a significant difference between asthma severity groups or healthy controls). (A-B) Consensus matrices for models with optimal number of clusters. Six clusters (K=6; A) and ten clusters (K=10; B) visualized in heatmaps. Values are between 0 (shown in white; meaning two samples are never clustered together across the 1000 iterations) and 1 (shown in dark blue; meaning two samples are always clustered together across the 1000 iterations). (C) Consensus CDF plot for cluster models of two (K=2) and up to ten (K=10) clusters. (D) Delta area plot with the relative change in the area under the CDF curve when comparing models with "K" clusters and "K -1 " clusters. For K=2, the total area under the curve is shown. (E) Cluster-consensus plot where, for a given cluster model K, the mean of all pairwise consensus values is higher if subjects are more often clustered together. Figure S3. Importance of proteins for classification of clusters. The Boruta algorithm identifies all variables with significant importance for the classification of six clusters and furthermore defines each variable as confirmed (n=108, with increasing importance from left to right), tentatively important (n=5) or unimportant (n=26).