Research Protocol for an Observational Health Data Analysis on the Adverse Events of Systemic Treatment in Patients with Metastatic Hormone-sensitive Prostate Cancer: Big Data Analytics Using the PIONEER Platform

Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in "real-life" patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.

Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data

BACKGROUND

Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics.

OBJECTIVE

To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data.

DESIGN, SETTING, AND PARTICIPANTS

From an initial cohort of >100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data.

RESULTS AND LIMITATIONS

The most common comorbidities were hypertension (35-73%), obesity (9.2-54%), and type 2 diabetes (11-28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12-25%) and emergency department visits (10-14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent.

CONCLUSIONS

Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data.

PATIENT SUMMARY

Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.

Suboptimal Response to Biologics in Severe Asthma-A Marker of Humoral Immunodeficiencies

BACKGROUND

Asthmatic patients with antibody deficiencies (AD) have more severe disease and higher risk of exacerbations. No data exist about the efficacy of biologics in severe asthma (SA) patients with AD.

OBJECTIVE

To evaluate the efficacy of biologics in SA patients with and without AD.

METHODS

A case-control real-life study was conducted including 68 patients divided into 2 groups: group 1 with SA-AD and group 2 with SA.

RESULTS

Treatment with biologics for 6 months was effective for decreasing the number of exacerbations, hospitalizations, and emergency department (ED) visits and improving the Asthma Control Questionnaire (ACQ) score; biologics also proved a systemic corticosteroid-sparing effect. Despite benefits, the number of exacerbations, hospitalizations, and ED visits, the mean ACQ score, and the cumulative dose of systemic corticosteroids remain higher in group 1 than in group 2, with lower lung function parameters. The rates of responses in group 1 were inferior to those in group 2, with a decrease by ≥50% of exacerbation rate in 76% versus 97% of patients (P = .006), no hospitalization in 44% versus 91% of patients (P < .001), no ED visit in 56% versus 82% of patients (P = .018), a significant improvement of the ACQ score by ≥0.5 in 68% versus 100% of patients (P < .001), and an increase of forced expiratory volume in the first second by >10% in 32% versus 65% of patients (P = .007).

CONCLUSIONS

Despite evident benefits, SA patients with AD have suboptimal responses to biologics compared with those immunocompetent. A multidisciplinary approach is necessary to optimize the management of these patients in practice.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods

Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.

Results

Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.

Discussion

The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

The role of inflammation in anxiety and depression in the European U-BIOPRED asthma cohorts

BACKGROUND

Growing evidence indicates high comorbid anxiety and depression in patients with asthma. However, the mechanisms underlying this comorbid condition remain unclear. The aim of this study was to investigate the role of inflammation in comorbid anxiety and depression in three asthma patient cohorts of the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project.

METHODS

U-BIOPRED was conducted by a European Union consortium of 16 academic institutions in 11 European countries. A subset dataset from subjects with valid anxiety and depression measures and a large blood biomarker dataset were analysed, including 198 non-smoking patients with severe asthma (SAn), 65 smoking patients with severe asthma (SAs), 61 non-smoking patients with mild-to-moderate asthma (MMA), and 20 healthy non-smokers (HC). The Hospital Anxiety and Depression Scale was used to measure anxiety and depression and a series of inflammatory markers were analysed by the SomaScan v3 platform (SomaLogic, Boulder, Colo). ANOVA and the Kruskal-Wallis test were used for multiple-group comparisons as appropriate.

RESULTS

There were significant group effects on anxiety and depression among the four cohort groups (p < 0.05). Anxiety and depression of SAn and SAs groups were significantly higher than that of MMA and HC groups (p < 0.05. There were significant differences in serum IL6, MCP1, CCL18, CCL17, IL8, and Eotaxin among the four groups (p < 0.05). Depression was significantly associated with IL6, MCP1, CCL18 level, and CCL17; whereas anxiety was associated with CCL17 only (p < 0.05).

CONCLUSIONS

The current study suggests that severe asthma patients are associated with higher levels of anxiety and depression, and inflammatory responses may underlie this comorbid condition.

Unanswered questions in prostate cancer - findings of an international multi-stakeholder consensus by the PIONEER consortium

PIONEER is a European network of excellence for big data in prostate cancer consisting of 37 private and public stakeholders from 9 countries across Europe. Many progresses have been done in prostate cancer management, but unanswered questions in the field still exist, and big data could help to answer these questions. The PIONEER consortium conducted a two-round modified Delphi survey aiming at building consensus between two stakeholder groups - health-care professionals and patients with prostate cancer - about the most important questions in the field of prostate cancer to be answered using big data. Respondents were asked to consider what would be the effect of answering the proposed questions on improving diagnosis and treatment outcomes for patients with prostate cancer and to score these questions on a scale of 1 (not important) to 9 (critically important). The mean percentage of participants who scored each of the proposed questions as critically important was calculated across the two stakeholder groups and used to rank the questions and identify the highest scoring questions in the critically important category. The identification of questions in prostate cancer that are important to various stakeholders will help the PIONEER consortium to provide answers to these questions to improve the clinical care of patients with prostate cancer.

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.

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.

Application of new proteomic panel in two established European asthma cohorts identifies plasma proteins associated with disease severity independently of Type-2 inflammation, suggesting potentially useful novel biomarkers and therapeutic targets.https://bit.ly/3jtTq5m

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Instability of sputum molecular phenotypes in U-BIOPRED severe asthma

The Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project has described phenotypic differences of severe asthma using a systems biology approach. We obtained three molecular phenotypes termed transcription-associated clusters (TACs) using hierarchical clustering of differentially expressed transcripts between T2-high and T2-low [1]. TAC1 was characterised by receptors IL33R, CCR3 and TSLPR, with the highest enrichment of gene signatures for IL-13/type-2 (T2) inflammation with sputum eosinophilia; TAC2 by inflammasome-associated genes, interferon-α (IFN-α) and tumour necrosis factor-α (TNF-α)-associated genes with sputum neutrophilia; and TAC3 by metabolic and mitochondrial function genes with pauci-granulocytic inflammation. Given that sputum eosinophilia may vary with time in many asthmatic subjects [2, 3], we hypothesised that TAC status may also change with time.

At 1 year, 45% of severe asthma change molecular phenotype as determined by sputum transcriptomic analysis. Together with concomitant shift in sputum granulocytic markers, this may indicate variability of driving mechanisms in this unstable group.https://bit.ly/35aj489

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.

Host Transcription Profile in Nasal Epithelium and Whole Blood of Hospitalized Children Under 2 Years of Age With Respiratory Syncytial Virus Infection

Background

Most insights into the cascade of immune events after acute respiratory syncytial virus (RSV) infection have been obtained from animal experiments or in vitro models.

Methods

In this study, we investigated host gene expression profiles in nasopharyngeal (NP) swabs and whole blood samples during natural RSV and rhinovirus (hRV) infection (acute versus early recovery phase) in 83 hospitalized patients <2 years old with lower respiratory tract infections.

Results

Respiratory syncytial virus infection induced strong and persistent innate immune responses including interferon signaling and pathways related to chemokine/cytokine signaling in both compartments. Interferon-α/β, NOTCH1 signaling pathways and potential biomarkers HIST1H4E, IL7R, ISG15 in NP samples, or BCL6, HIST2H2AC, CCNA1 in blood are leading pathways and hub genes that were associated with both RSV load and severity. The observed RSV-induced gene expression patterns did not differ significantly in NP swab and blood specimens. In contrast, hRV infection did not as strongly induce expression of innate immunity pathways, and significant differences were observed between NP swab and blood specimens.

Conclusions

We conclude that RSV induced strong and persistent innate immune responses and that RSV severity may be related to development of T follicular helper cells and antiviral inflammatory sequelae derived from high activation of BCL6.

"T2-high" in severe asthma related to blood eosinophil, exhaled nitric oxide and serum periostin

Type-2 (T2) immune responses in airway epithelial cells (AECs) classifies mild-moderate asthma into a T2-high phenotype. We examined whether currently available clinical biomarkers can predict AEC-defined T2-high phenotype within the U-BIOPRED cohort.The transcriptomic profile of AECs obtained from brushings of 103 patients with asthma and 44 healthy controls was obtained and gene set variation analysis used to determine the relative expression score of T2 asthma using a signature from interleukin (IL)-13-exposed AECs.37% of asthmatics (45% nonsmoking severe asthma, n=49; 33% of smoking or ex-smoking severe asthma, n=18; and 28% mild-moderate asthma, n=36) were T2-high using AEC gene expression. They were more symptomatic with higher exhaled nitric oxide fraction (F _eNO) and blood and sputum eosinophils, but not serum IgE or periostin. Sputum eosinophilia correlated best with the T2-high signature. F _eNO (≥30 ppb) and blood eosinophils (≥300 cells·µL^-1) gave a moderate prediction of T2-high asthma. Sputum IL-4, IL-5 and IL-13 protein levels did not correlate with gene expression.T2-high severe asthma can be predicted to some extent from raised levels of F _eNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors. Better bedside biomarkers are needed to detect T2-high.

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.

Enhanced oxidative stress in smoking and ex-smoking severe asthma in the U-BIOPRED cohort

Oxidative stress is believed to be a major driver of inflammation in smoking asthmatics. The U-BIOPRED project recruited a cohort of Severe Asthma smokers/ex-smokers (SAs/ex) and non-smokers (SAn) with extensive clinical and biomarker information enabling characterization of these subjects. We investigated oxidative stress in severe asthma subjects by analysing urinary 8-iso-PGF2α and the mRNA-expression of the main pro-oxidant (NOX2; NOSs) and anti-oxidant (SODs; CAT; GPX1) enzymes in the airways of SAs/ex and SAn. All the severe asthma U-BIOPRED subjects were further divided into current smokers with severe asthma (CSA), ex-smokers with severe asthma (ESA) and non-smokers with severe asthma (NSA) to deepen the effect of active smoking. Clinical data, urine and sputum were obtained from severe asthma subjects. A bronchoscopy to obtain bronchial biopsy and brushing was performed in a subset of subjects. The main clinical data were analysed for each subset of subjects (urine-8-iso-PGF2α; IS-transcriptomics; BB-transcriptomics; BBr-transcriptomics). Urinary 8-iso-PGF2α was quantified using mass spectrometry. Sputum, bronchial biopsy and bronchial brushing were processed for mRNA expression microarray analysis. Urinary 8-iso-PGF2α was increased in SAs/ex, median (IQR) = 31.7 (24.5-44.7) ng/mmol creatinine, compared to SAn, median (IQR) = 26.6 (19.6-36.6) ng/mmol creatinine (p< 0.001), and in CSA, median (IQR) = 34.25 (24.4-47.7), vs. ESA, median (IQR) = 29.4 (22.3-40.5), and NSA, median (IQR) = 26.5 (19.6-16.6) ng/mmol creatinine (p = 0.004). Sputum mRNA expression of NOX2 was increased in SAs/ex compared to SAn (probe sets 203922_PM_s_at fold-change = 1.05 p = 0.006; 203923_PM_s_at fold-change = 1.06, p = 0.003; 233538_PM_s_at fold-change = 1.06, p = 0.014). The mRNA expression of antioxidant enzymes were similar between the two severe asthma cohorts in all airway samples. NOS2 mRNA expression was decreased in bronchial brushing of SAs/ex compared to SAn (fold-change = -1.10; p = 0.029). NOS2 mRNA expression in bronchial brushing correlated with FeNO (Kendal's Tau = 0.535; p< 0.001). From clinical and inflammatory analysis, FeNO was lower in CSA than in ESA in all the analysed subject subsets (p< 0.01) indicating an effect of active smoking. Results about FeNO suggest its clinical limitation, as inflammation biomarker, in severe asthma active smokers. These data provide evidence of greater systemic oxidative stress in severe asthma smokers as reflected by a significant changes of NOX2 mRNA expression in the airways, together with elevated urinary 8-iso-PGF2α in the smokers/ex-smokers group. Trial registration ClinicalTrials.gov-Identifier: NCT01976767.

Epithelial IL-6 trans-signaling defines a new asthma phenotype with increased airway inflammation

BACKGROUND

Although several studies link high levels of IL-6 and soluble IL-6 receptor (sIL-6R) to asthma severity and decreased lung function, the role of IL-6 trans-signaling (IL-6TS) in asthmatic patients is unclear.

OBJECTIVE

We sought to explore the association between epithelial IL-6TS pathway activation and molecular and clinical phenotypes in asthmatic patients.

METHODS

An IL-6TS gene signature obtained from air-liquid interface cultures of human bronchial epithelial cells stimulated with IL-6 and sIL-6R was used to stratify lung epithelial transcriptomic data (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes [U-BIOPRED] cohorts) by means of hierarchical clustering. IL-6TS-specific protein markers were used to stratify sputum biomarker data (Wessex cohort). Molecular phenotyping was based on transcriptional profiling of epithelial brushings, pathway analysis, and immunohistochemical analysis of bronchial biopsy specimens.

RESULTS

Activation of IL-6TS in air-liquid interface cultures reduced epithelial integrity and induced a specific gene signature enriched in genes associated with airway remodeling. The IL-6TS signature identified a subset of patients with IL-6TS-high asthma with increased epithelial expression of IL-6TS-inducible genes in the absence of systemic inflammation. The IL-6TS-high subset had an overrepresentation of frequent exacerbators, blood eosinophilia, and submucosal infiltration of T cells and macrophages. In bronchial brushings Toll-like receptor pathway genes were upregulated, whereas expression of cell junction genes was reduced. Sputum sIL-6R and IL-6 levels correlated with sputum markers of remodeling and innate immune activation, in particular YKL-40, matrix metalloproteinase 3, macrophage inflammatory protein 1β, IL-8, and IL-1β.

CONCLUSIONS

Local lung epithelial IL-6TS activation in the absence of type 2 airway inflammation defines a novel subset of asthmatic patients and might drive airway inflammation and epithelial dysfunction in these patients.

Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.

A computational framework for complex disease stratification from multiple large-scale datasets

BACKGROUND

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.

METHODS

The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.

RESULTS

We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.

CONCLUSIONS

This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Sputum proteomics and airway cell transcripts of current and ex-smokers with severe asthma in U-BIOPRED: an exploratory analysis

Severe asthma patients with a significant smoking history have airflow obstruction with reported neutrophilia. We hypothesise that multi-omic analysis will enable the definition of smoking and ex-smoking severe asthma molecular phenotypes.The U-BIOPRED cohort of severe asthma patients, containing current-smokers (CSA), ex-smokers (ESA), nonsmokers and healthy nonsmokers was examined. Blood and sputum cell counts, fractional exhaled nitric oxide and spirometry were obtained. Exploratory proteomic analysis of sputum supernatants and transcriptomic analysis of bronchial brushings, biopsies and sputum cells was performed.Colony-stimulating factor (CSF)2 protein levels were increased in CSA sputum supernatants, with azurocidin 1, neutrophil elastase and CXCL8 upregulated in ESA. Phagocytosis and innate immune pathways were associated with neutrophilic inflammation in ESA. Gene set variation analysis of bronchial epithelial cell transcriptome from CSA showed enrichment of xenobiotic metabolism, oxidative stress and endoplasmic reticulum stress compared to other groups. CXCL5 and matrix metallopeptidase 12 genes were upregulated in ESA and the epithelial protective genes, mucin 2 and cystatin SN, were downregulated.Despite little difference in clinical characteristics, CSA were distinguishable from ESA subjects at the sputum proteomic level, with CSA patients having increased CSF2 expression and ESA patients showing sustained loss of epithelial barrier processes.

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.

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.

A Severe Asthma Disease Signature from Gene Expression Profiling of Peripheral Blood from U-BIOPRED Cohorts

RATIONALE

Stratification of asthma at the molecular level, especially using accessible biospecimens, could greatly enable patient selection for targeted therapy.

OBJECTIVES

To determine the value of blood analysis to identify transcriptional differences between clinically defined asthma and nonasthma groups, identify potential patient subgroups based on gene expression, and explore biological pathways associated with identified differences.

METHODS

Transcriptomic profiles were generated by microarray analysis of blood from 610 patients with asthma and control participants in the U-BIOPRED (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes) study. Differentially expressed genes (DEGs) were identified by analysis of variance, including covariates for RNA quality, sex, and clinical site, and Ingenuity Pathway Analysis was applied. Patient subgroups based on DEGs were created by hierarchical clustering and topological data analysis.

MEASUREMENTS AND MAIN RESULTS

A total of 1,693 genes were differentially expressed between patients with severe asthma and participants without asthma. The differences from participants without asthma in the nonsmoking severe asthma and mild/moderate asthma subgroups were significantly related (r = 0.76), with a larger effect size in the severe asthma group. The majority of, but not all, differences were explained by differences in circulating immune cell populations. Pathway analysis showed an increase in chemotaxis, migration, and myeloid cell trafficking in patients with severe asthma, decreased B-lymphocyte development and hematopoietic progenitor cells, and lymphoid organ hypoplasia. Cluster analysis of DEGs led to the creation of subgroups among the patients with severe asthma who differed in molecular responses to oral corticosteroids.

CONCLUSIONS

Blood gene expression differences between clinically defined subgroups of patients with asthma and individuals without asthma, as well as subgroups of patients with severe asthma defined by transcript profiles, show the value of blood analysis in stratifying patients with asthma and identifying molecular pathways for further study. Clinical trial registered with www.clinicaltrials.gov (NCT01982162).

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.

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.

Adaptation of a Bioinformatics Microarray Analysis Workflow for a Toxicogenomic Study in Rainbow Trout

Sex steroids play a key role in triggering sex differentiation in fish, the use of exogenous hormone treatment leading to partial or complete sex reversal. This phenomenon has attracted attention since the discovery that even low environmental doses of exogenous steroids can adversely affect gonad morphology (ovotestis development) and induce reproductive failure. Modern genomic-based technologies have enhanced opportunities to find out mechanisms of actions (MOA) and identify biomarkers related to the toxic action of a compound. However, high throughput data interpretation relies on statistical analysis, species genomic resources, and bioinformatics tools. The goals of this study are to improve the knowledge of feminisation in fish, by the analysis of molecular responses in the gonads of rainbow trout fry after chronic exposure to several doses (0.01, 0.1, 1 and 10 μg/L) of ethynylestradiol (EE2) and to offer target genes as potential biomarkers of ovotestis development. We successfully adapted a bioinformatics microarray analysis workflow elaborated on human data to a toxicogenomic study using rainbow trout, a fish species lacking accurate functional annotation and genomic resources. The workflow allowed to obtain lists of genes supposed to be enriched in true positive differentially expressed genes (DEGs), which were subjected to over-representation analysis methods (ORA). Several pathways and ontologies, mostly related to cell division and metabolism, sexual reproduction and steroid production, were found significantly enriched in our analyses. Moreover, two sets of potential ovotestis biomarkers were selected using several criteria. The first group displayed specific potential biomarkers belonging to pathways/ontologies highlighted in the experiment. Among them, the early ovarian differentiation gene foxl2a was overexpressed. The second group, which was highly sensitive but not specific, included the DEGs presenting the highest fold change and lowest p-value of the statistical workflow output. The methodology can be generalized to other (non-model) species and various types of microarray platforms.

Intersex occurrence in rainbow trout (Oncorhynchus mykiss) male fry chronically exposed to ethynylestradiol

This study aimed to investigate the male-to-female morphological and physiological transdifferentiation process in rainbow trout (Oncorhynchus mykiss) exposed to exogenous estrogens. The first objective was to elucidate whether trout develop intersex gonads under exposure to low levels of estrogen. To this end, the gonads of an all-male population of fry exposed chronically (from 60 to 136 days post fertilization – dpf) to several doses (from environmentally relevant 0.01 µg/L to supra-environmental levels: 0.1, 1 and 10 µg/L) of the potent synthetic estrogen ethynylestradiol (EE2) were examined histologically. The morphological evaluations were underpinned by the analysis of gonad steroid (testosterone, estradiol and 11-ketotestosterone) levels and of brain and gonad gene expression, including estrogen-responsive genes and genes involved in sex differentiation in (gonads: cyp19a1a, ER isoforms, vtg, dmrt1, sox9a2; sdY; cyp11b; brain: cyp19a1b, ER isoforms). Intersex gonads were observed from the first concentration used (0.01 µg EE2/L) and sexual inversion could be detected from 0.1 µg EE2/L. This was accompanied by a linear decrease in 11-KT levels, whereas no effect on E2 and T levels was observed. Q-PCR results from the gonads showed downregulation of testicular markers (dmrt1, sox9a2; sdY; cyp11b) with increasing EE2 exposure concentrations, and upregulation of the female vtg gene. No evidence was found for a direct involvement of aromatase in the sex conversion process. The results from this study provide evidence that gonads of male trout respond to estrogen exposure by intersex formation and, with increasing concentration, by morphological and physiological conversion to phenotypic ovaries. However, supra-environmental estrogen concentrations are needed to induce these changes.

Meta-analysis and gene set analysis of archived microarrays suggest implication of the spliceosome in metastatic and hypoxic phenotypes

We propose to make use of the wealth of underused DNA chip data available in public repositories to study the molecular mechanisms behind the adaptation of cancer cells to hypoxic conditions leading to the metastatic phenotype. We have developed new bioinformatics tools and adapted others to identify with maximum sensitivity those genes which are expressed differentially across several experiments. The comparison of two analytical approaches, based on either Over Representation Analysis or Functional Class Scoring, by a meta-analysis-based approach, led to the retrieval of known information about the biological situation - thus validating the model - but also more importantly to the discovery of the previously unknown implication of the spliceosome, the cellular machinery responsible for mRNA splicing, in the development of metastasis.

Combined effects of deltamethrin, temperature and salinity on oxidative stress biomarkers and acetylcholinesterase activity in the black tiger shrimp (Penaeus monodon)

This study aimed to investigate the interactions of two abiotic factors (temperature and salinity) and deltamethrin (pyrethroid pesticide) exposure on some oxidative stress biomarkers as well as on acetylcholinesterase activity (AChE) in hepatopancreas, gills and muscle of black tiger shrimp (Penaeus monodon). A combination of three temperatures (24, 29 and 34°C), two salinities (15 and 25 ppt), and the absence or presence of 0.1 μg L(-1) deltamethrin was applied on shrimp during 4 d under laboratory conditions. Lipid peroxidation level (LPO) and glutathione S-transferase activity (GST) were not affected by combined effect of temperature, salinity and deltamethrin in any of the studied tissues. Deltamethrin impaired other tested oxidative stress biomarkers, i.e. total glutathione (tGSH), catalase (CAT), glutathione peroxidase (GPx). tGSH level significantly increased in hepatopancreas due to deltamethrin exposure mainly at 34°C, while pesticide effects on tGSH and CAT activity in gills were influenced by both temperature and salinity. In addition, GPx activity in hepatopancreas decreased after deltamethrin treatment mainly at 24°C. Finally, AChE in muscle was strongly inhibited by deltamethrin at all tested temperatures and salinities. These novel findings demonstrate that interactions between abiotic factors and a commonly used pesticide exposure should be taken into account when analyzing some widespread biomarkers in black tiger shrimp.

gViz, a novel tool for the visualization of co-expression networks

BACKGROUND

The quantity of microarray data available on the Internet has grown dramatically over the past years and now represents millions of Euros worth of underused information. One way to use this data is through co-expression analysis. To avoid a certain amount of bias, such data must often be analyzed at the genome scale, for example by network representation. The identification of co-expression networks is an important means to unravel gene to gene interactions and the underlying functional relationship between them. However, it is very difficult to explore and analyze a network of such dimensions. Several programs (Cytoscape, yEd) have already been developed for network analysis; however, to our knowledge, there are no available GraphML compatible programs.

FINDINGS

We designed and developed gViz, a GraphML network visualization and exploration tool. gViz is built on clustering coefficient-based algorithms and is a novel tool to visualize and manipulate networks of co-expression interactions among a selection of probesets (each representing a single gene or transcript), based on a set of microarray co-expression data stored as an adjacency matrix.

CONCLUSIONS

We present here gViz, a software tool designed to visualize and explore large GraphML networks, combining network theory, biological annotation data, microarray data analysis and advanced graphical features.

PathEx: a novel multi factors based datasets selector web tool

Background

Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.

Description

Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.

Conclusion

This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.

A benchmark for statistical microarray data analysis that preserves actual biological and technical variance

BACKGROUND

Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.

RESULTS

Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.

CONCLUSIONS

Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better.

AVAILABILITY

The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.