Network Analysis in the Investigation of Chronic Respiratory Diseases. From Basics to Application

Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

The current management of patients with multimorbidity is suboptimal, with either a single-disease approach to care or treatment guideline adaptations that result in poor adherence due to their complexity. Although this has resulted in calls for more holistic and personalized approaches to prescribing, progress toward these goals has remained slow. With the rapid advancement of machine learning (ML) methods, promising approaches now also exist to accelerate the advance of precision medicine in multimorbidity. These include analyzing disease comorbidity networks, using knowledge graphs that integrate knowledge from different medical domains, and applying network analysis and graph ML. Multimorbidity disease networks have been used to improve disease diagnosis, treatment recommendations, and patient prognosis. Knowledge graphs that combine different medical entities connected by multiple relationship types integrate data from different sources, allowing for complex interactions and creating a continuous flow of information. Network analysis and graph ML can then extract the topology and structure of networks and reveal hidden properties, including disease phenotypes, network hubs, and pathways; predict drugs for repurposing; and determine safe and more holistic treatments. In this article, we describe the basic concepts of creating bipartite and unipartite disease and patient networks and review the use of knowledge graphs, graph algorithms, graph embedding methods, and graph ML within the context of multimorbidity. Specifically, we provide an overview of the application of graph theory for studying multimorbidity, the methods employed to extract knowledge from graphs, and examples of the application of disease networks for determining the structure and pathways of multimorbidity, identifying disease phenotypes, predicting health outcomes, and selecting safe and effective treatments. In today's modern data-hungry, ML-focused world, such network-based techniques are likely to be at the forefront of developing robust clinical decision support tools for safer and more holistic approaches to treating older patients with multimorbidity.

Pathogenesis of chronic obstructive pulmonary disease: understanding the contributions of gene-environment interactions across the lifespan

The traditional view of chronic obstructive pulmonary disease (COPD) as a self-inflicted disease caused by tobacco smoking in genetically susceptible individuals has been challenged by recent research findings. COPD can instead be understood as the potential end result of the accumulation of gene-environment interactions encountered by an individual over the life course. Integration of a time axis in pathogenic models of COPD is necessary because the biological responses to and clinical consequences of different exposures might vary according to both the age of an individual at which a given gene-environment interaction occurs and the cumulative history of previous gene-environment interactions. Future research should aim to understand the effects of dynamic interactions between genes (G) and the environment (E) by integrating information from basic omics (eg, genomics, epigenomics, proteomics) and clinical omics (eg, phenomics, physiomics, radiomics) with exposures (the exposome) over time (T)-an approach that we refer to as GETomics. In the context of this approach, we argue that COPD should be viewed not as a single disease, but as a clinical syndrome characterised by a recognisable pattern of chronic symptoms and structural or functional impairments due to gene-environment interactions across the lifespan that influence normal lung development and ageing.

Inhaled Steroids, Circulating Eosinophils, Chronic Airway Infection, and Pneumonia Risk in Chronic Obstructive Pulmonary Disease. A Network Analysis

Rationale: Treatment of chronic obstructive pulmonary disease (COPD) with inhaled corticosteroids (ICS) is controversial, because it can reduce the risk of future exacerbations of the disease at the expense of increasing the risk of pneumonia.Objectives: To assess the relationship between the presence of chronic bronchial infection (CBI), reduced number of circulating eosinophils, ICS treatment, and the risk of pneumonia in patients with COPD.Methods: This was a post hoc long-term observational study of an historical cohort of 201 patients with COPD (Global Initiative for Chronic Obstructive Lung Disease II-IV) who were carefully characterized (including airway microbiology) and followed for a median of 84 months. Results were analyzed by multivariate Cox regression and network analysis.Measurements and Main Results: Mean age was 70.3 years, 90.5% of patients were male, mean FEV₁ was 49%, 71.6% of patients were treated with ICS, 57.2% of them had bronchiectasis, and 20.9% had <100 blood eosinophils/μl. Pathogenic microorganisms were isolated in 42.3% of patients, and 22.4% of patients fulfilled the definition of CBI. During follow-up, 38.8% of patients suffered one or more episodes of pneumonia, with CBI (hazard ratio [HR], 1.635) and <100 eosinophils/μl (HR, 1.975) being independently associated with the risk of pneumonia, particularly when both coexist (HR, 3.126). ICS treatment increased the risk of pneumonia in those patients with <100 eosinophils/μl and CBI (HR, 2.925).Conclusions: Less than 100 circulating eosinophils/μl combined with the presence of CBI increase the risk of pneumonia in patients with COPD treated with ICS.

Dissociation between airway and systemic autoantibody responses in chronic obstructive pulmonary disease

Background

Autoimmune processes have been implicated in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the relationship between airway and systemic autoantibody responses remains unclear. The aim of this study was to elucidate this relationship in patients with stable COPD by investigating the correlation patterns between sputum and serum autoantibodies.

Methods

In this cross-sectional study, sputum supernatant and serum obtained from 47 patients with stable COPD were assayed for the presence of IgG antibodies against ten autoantigens: Smith antigen (Sm), ribosomal phosphoprotein P0 (P0), Ro/Sjögren syndrome type A antigen (Ro/SSA), La/Sjögren syndrome type B antigen (La/SSB), DNA topoisomerase I (Scl-70), histidyl-tRNA synthetase (Jo-1), U1 small nuclear ribonucleoprotein (U1-SnRNP), thyroid peroxidase (TPO), proteinase-3 (PR3), and myeloperoxidase (MPO). A second cohort of 55 stable COPD patients was recruited for validation, and a group of 59 non-COPD controls and a group of 20 connective-tissue disease-associated interstitial lung disease (CTD-ILD) patients were also recruited for comparison. Hierarchical clustering and network analysis were used to evaluate the correlation patterns between sputum and serum autoantibody profiles.

Results

Both hierarchical clustering and network analysis showed that sputum and serum autoantibody profiles were distinct in either analytic COPD cohort or validation cohort. In contrast, the autoantibodies of the two compartments in non-COPD controls and CTD-ILD patients were inadequately distinguished using either hierarchical clustering or network analysis. Many autoantibodies in the sputum were found to have significant correlations with lung function, symptom score and frequency of prior exacerbations in COPD patients, but the antibodies in the serum were not.

Conclusions

We observed a dissociation between sputum autoantibodies and serum autoantibodies in patients with stable COPD, suggesting that airway and systemic immune status may play very different roles in the disease. Sputum autoantibodies are more clinically relevant than serum autoantibodies. Focusing on airway autoimmunity may help improve understanding of the immunopathological mechanism of COPD.

Sputum and serum autoantibody profiles and their clinical correlation patterns in COPD patients with and without eosinophilic airway inflammation

Background

Autoimmunity plays a role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the autoantibody responses and their clinical correlation patterns in COPD patients with and without airway eosinophilic inflammation are unknown. The aim of this study was to compare the autoantibody profiles and their clinical associations in stable COPD patients, stratified by airway inflammatory phenotypes.

Methods

Matched sputum and serum, obtained from 62 stable COPD patients and 14 age-matched controls, were assayed for the presence of IgG and IgM antibodies against 13 autoantigens using protein array. A sputum eosinophil count ≥3% was used as cut-off value to stratify COPD patients into eosinophilic and non-eosinophilic groups. Correlation network analysis was used to evaluate the correlation patterns among autoantibody and clinical variables in each group.

Results

There were no significant differences of clinical parameters and autoantibody levels between the two COPD groups. In non-eosinophilic COPD, sputum anti-CytochromeC_IgG and anti-Aggrecan_IgM were significantly higher than those in healthy controls, and prior exacerbation was positively associated with lung function and sputum anti-Collagen-IV_IgG. While in eosinophilic COPD, sputum/serum anti-heat shock protein (HSP)47_IgG, serum anti-HSP70_IgG and serum anti-Amyloid-beta_IgG were significantly lower than those in healthy controls, and no significant correlation between prior exacerbations and lung function was found. Differences were also observed in network hubs, with the network for non-eosinophilic COPD possessing 9 hubs comprising two lung function parameters and seven autoantibodies, compared with eosinophilic COPD possessing 12 hubs all comprising autoantibodies.

Conclusions

Autoantibody responses were heterogeneous and differentially correlated with the exacerbation risk and other clinical parameters in COPD patients of different inflammatory phenotypes. These findings provide useful insight into the need for personalized management for preventing COPD exacerbations.

An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies

Background

Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases.

Results

In particular, a set of nine representative disease-related genes was detected, these being the most significant genes in relation to their roles in channelopathies. In this way we attested the implication of some voltage-gated sodium (SCN1A, SCN2A, SCN4A, SCN4B, SCN5A, SCN9A) and potassium (KCNQ2, KCNH2) channels in cardiovascular diseases, epilepsies, febrile seizures, headache disorders, neuromuscular, neurodegenerative diseases or neurobehavioral manifestations. We also revealed the role of Ankyrin-G (ANK3) in the neurodegenerative and neurobehavioral disorders as well as the implication of these genes in other systems, such as the immunological or endocrine systems.

Conclusions

This research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases.

Methods

An initial gene pool is previously defined by searching general databases under a specific semantic framework. From the resulting interaction network, a subset of genes are identified as the most relevant through the workflow that includes centrality measures and other filtering and enrichment databases.

The LEAD (Lung, Heart, Social, Body) Study: Objectives, Methodology, and External Validity of the Population-Based Cohort Study

BACKGROUND

The Lung, hEart, sociAl, boDy (LEAD) Study (ClinicalTrials.gov; NCT01727518; http://clinicaltrials.gov) is a longitudinal, observational, population-based Austrian cohort that aims to investigate the relationship between genetic, environmental, social, developmental and ageing factors influencing respiratory health and comorbidities through life. The general working hypothesis of LEAD is the interaction of these genetic, environmental and socioeconomic factors influences lung development and ageing, the risk of occurrence of several non-communicable diseases (respiratory, cardiovascular, metabolic and neurologic), as well as their phenotypic (ie, clinical) presentation.

METHODS

LEAD invited from 2011-2016 a random sample (stratified by age, gender, residential area) of Vienna inhabitants (urban cohort) and all the inhabitants of six villages from Lower Austria (rural cohort). Participants will be followed-up every four years. A number of investigations and measurements were obtained in each of the four domains of the study (Lung, hEart, sociAl, boDy) including data to screen for lung, cardiovascular and metabolic diseases, osteoporosis, and cognitive function. Blood and urine samples are stored in a biobank for future investigations.

RESULTS

A total of 11.423 males (47.6%) and females (52.4%), aged 6-80 years have been included in the cohort. Compared to governmental statistics, the external validity of LEAD with respect to age, gender, citizenship, and smoking status was high.

CONCLUSIONS

In conclusion, the LEAD cohort has been established following high quality standards; it is representative of the Austrian population and offers a platform to understand lung development and ageing as a key mechanism of human health both in early and late adulthood.

Treatable traits: a new paradigm for 21st century management of chronic airway diseases: Treatable Traits Down Under International Workshop report

"Treatable traits" have been proposed as a new paradigm for the management of airway diseases, particularly complex disease, which aims to apply personalised medicine to each individual to improve outcomes. Moving new treatment approaches from concepts to practice is challenging, but necessary. In an effort to accelerate progress in research and practice relating to the treatable traits approach, the Treatable Traits Down Under International Workshop was convened in Melbourne, Australia in May 2018. Here, we report the key concepts and research questions that emerged in discussions during the meeting. We propose a programme of research that involves gaining international consensus on candidate traits, recognising the prevalence of traits, and identifying a potential hierarchy of traits based on their clinical impact and responsiveness to treatment. We also reflect on research methods and designs that can generate new knowledge related to efficacy of the treatable traits approach and consider multidisciplinary models of care that may aid its implementation into practice.

Prospective observational study in patients with obstructive lung disease: NOVELTY design

Asthma and chronic obstructive pulmonary disease (COPD) have overlapping clinical features and share pathobiological mechanisms but are often considered distinct disorders. Prospective, observational studies across asthma, COPD and asthma–COPD overlap are limited. NOVELTY is a global, prospective observational 3-year study enrolling ∼12 000 patients ≥12 years of age from primary and specialist clinical practices in 19 countries (ClinicalTrials.gov identifier: NCT02760329).

NOVELTY's primary objectives are to describe patient characteristics, treatment patterns and disease burden over time, and to identify phenotypes and molecular endotypes associated with differential outcomes over time in patients with a diagnosis/suspected diagnosis of asthma and/or COPD. NOVELTY aims to recruit real-world patients, unlike clinical studies with restrictive inclusion/exclusion criteria.

Data collected at yearly intervals include clinical assessments, spirometry, biospecimens, patient-reported outcomes (PROs) and healthcare utilisation (HCU). PROs and HCU will also be collected 3-monthly via internet/telephone. Data will be used to identify phenotypes and endotypes associated with different trajectories for symptom burden, clinical progression or remission and HCU. Results may allow patient classification across obstructive lung disease by clinical outcomes and biomarker profile, rather than by conventional diagnostic labels and severity categories.

NOVELTY will provide a rich data source on obstructive lung disease, to help improve patient outcomes and aid novel drug development.

NOVELTY is a global study to characterise patients with asthma and/or COPD and identify novel phenotypes and endotypeshttp://ow.ly/QFiH30n3IBF

Stem cell-based Lung-on-Chips: The best of both worlds?

Pathologies of the respiratory system such as lung infections, chronic inflammatory lung diseases, and lung cancer are among the leading causes of morbidity and mortality, killing one in six people worldwide. Development of more effective treatments is hindered by the lack of preclinical models of the human lung that can capture the disease complexity, highly heterogeneous disease phenotypes, and pharmacokinetics and pharmacodynamics observed in patients. The merger of two novel technologies, Organs-on-Chips and human stem cell engineering, has the potential to deliver such urgently needed models. Organs-on-Chips, which are microengineered bioinspired tissue systems, recapitulate the mechanochemical environment and physiological functions of human organs while concurrent advances in generating and differentiating human stem cells promise a renewable supply of patient-specific cells for personalized and precision medicine. Here, we discuss the challenges of modeling human lung pathophysiology in vitro, evaluate past and current models including Organs-on-Chips, review the current status of lung tissue modeling using human pluripotent stem cells, explore in depth how stem-cell based Lung-on-Chips may advance disease modeling and drug testing, and summarize practical consideration for the design of Lung-on-Chips for academic and industry applications.

Precision medicine in COPD: where are we and where do we need to go?

Chronic obstructive pulmonary disease (COPD) was the fourth leading cause of death worldwide in 2015. Current treatments for patients ease discomfort and help decrease disease progression; however, none improve lung function or change mortality. COPD is heterogeneous in its molecular and clinical presentation, making it difficult to understand disease aetiology and define robust therapeutic strategies. Given the complexity of the disease we propose a precision medicine approach to understanding and better treating COPD. It is possible that multiOMICs can be used as a tool to integrate data from multiple fields. Moreover, analysis of electronic medical records could aid in the treatment of patients and in the predictions of outcomes. The Precision Medicine Initiative created in 2015 has made precision medicine approaches to treat disease a reality; one of these diseases being COPD.

Bone marrow characterization in COPD: a multi-level network analysis

Background

Bone marrow (BM) produces hematopoietic and progenitor cells that contribute to distant organ inflammation and repair. Chronic obstructive pulmonary disease (COPD) is characterized by defective lung repair. Yet, BM composition has not been previously characterized in COPD patients.

Methods

In this prospective and controlled study, BM was obtained by sternum fine-needle aspiration in 35 COPD patients and 25 healthy controls (10 smokers and 15 never-smokers). BM cell count and immunophenotype were determined by microscopy and flow cytometry, respectively. Circulating inflammatory (C-reactive protein, IL-6, IL-8) and repair markers (HGF, IGF, TGF-β, VEGF) were quantified by ELISA. Results were integrated by multi-level network correlation analysis.

Results

We found that: (1) there were no major significant pair wise differences between COPD patients and controls in the BM structural characteristics; (2) multi-level network analysis including patients and controls identifies a relation between immunity, repair and lung function not previously described, that remains in the COPD network but is absent in controls; and (3) this novel network identifies eosinophils as a potential mediator relating immunity and repair, particularly in patients with emphysema.

Conclusions

Overall, these results suggest that BM is activated in COPD with impaired repair capacity in patients with more emphysema and/or higher circulating eosinophils.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0824-x) contains supplementary material, which is available to authorized users.

Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients

Background

Chronic obstructive pulmonary disease (COPD) patients often show skeletal muscle dysfunction that has a prominent negative impact on prognosis. The study aims to further explore underlying mechanisms of skeletal muscle dysfunction as a characteristic systemic effect of COPD, potentially modifiable with preventive interventions (i.e. muscle training). The research analyzes network module associated pathways and evaluates the findings using independent measurements.

Methods

We characterized the transcriptionally active network modules of interacting proteins in the vastus lateralis of COPD patients (n = 15, FEV₁ 46 ± 12% pred, age 68 ± 7 years) and healthy sedentary controls (n = 12, age 65 ± 9  years), at rest and after an 8-week endurance training program. Network modules were functionally evaluated using experimental data derived from the same study groups.

Results

At baseline, we identified four COPD specific network modules indicating abnormalities in creatinine metabolism, calcium homeostasis, oxidative stress and inflammatory responses, showing statistically significant associations with exercise capacity (VO₂ peak, Watts peak, BODE index and blood lactate levels) (P < 0.05 each), but not with lung function (FEV₁). Training-induced network modules displayed marked differences between COPD and controls. Healthy subjects specific training adaptations were significantly associated with cell bioenergetics (P < 0.05) which, in turn, showed strong relationships with training-induced plasma metabolomic changes; whereas, effects of training in COPD were constrained to muscle remodeling.

Conclusion

In summary, altered muscle bioenergetics appears as the most striking finding, potentially driving other abnormal skeletal muscle responses.

Trial registration The study was based on a retrospectively registered trial (May 2017), ClinicalTrials.gov identifier: NCT03169270

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1405-y) contains supplementary material, which is available to authorized users.

Towards a global initiative for fibrosis treatment (GIFT)

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease characterised by increased scarring of lung tissue. Despite the recent introduction of novel drugs that slow disease progression, IPF remains a deadly disease, and the benefits of these new drugs differ markedly between patients.

Human diseases arise due to alterations in an almost limitless network of interconnected genes, proteins, metabolites, cells and tissues, in direct relationship with a continuously changing macro- or microenvironment. Systems biology is a novel research strategy that seeks to understand the structure and behaviour of the so-called “emergent properties” of complex systems, such as those involved in disease pathogenesis, which are most often overlooked when just one element of disease pathogenesis is observed in isolation.

This article summarises the debate that took place during a European Respiratory Society research seminar in Barcelona, Spain on December 15–16, 2016, which focused on how systems biology could generate new data by integrating the different IPF pathogenic levels of complexity. The main conclusion of the seminar was to create a global initiative to improve IPF outcomes by integrating cutting-edge international research that leverages systems biology to develop a precision medicine approach to tackle this devastating disease.

A novel call to action for implementing systems biology in IPF researchhttp://ow.ly/Is0A30gpnVb

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

Precision medicine in airway diseases: moving to clinical practice

On February 21, 2017, a European Respiratory Society research seminar held in Barcelona discussed how to best apply precision medicine to chronic airway diseases such as asthma and chronic obstructive pulmonary disease. It is now clear that both are complex and heterogeneous diseases, that often overlap and that both require individualised assessment and treatment. This paper summarises the presentations and discussions that took place during the seminar. Specifically, we discussed the need for a new taxonomy of human diseases, the role of different players in this scenario (exposome, genes, endotypes, phenotypes, biomarkers and treatable traits) and a number of unanswered key questions in the field. We also addressed how to deploy airway precision medicine in clinical practice today, both in primary and specialised care. Finally, we debated the type of research needed to move the field forward.

The EASI model: A first integrative computational approximation to the natural history of COPD

The natural history of chronic obstructive pulmonary disease (COPD) is still not well understood. Traditionally believed to be a self-inflicted disease by smoking, now we know that not all smokers develop COPD, that other inhaled pollutants different from cigarette smoke can also cause it, and that abnormal lung development can also lead to COPD in adulthood. Likewise, the inflammatory response that characterizes COPD varies significantly between patients, and not all of them perceive symptoms (mostly breathlessness) similarly. To investigate the variability and determinants of different “individual natural histories” of COPD, we developed a theoretical, multi-stage, computational model of COPD (EASI) that integrates dynamically and represents graphically the relationships between exposure (E) to inhaled particles and gases (smoking), the biological activity (inflammatory response) of the disease (A), the severity (S) of airflow limitation (FEV₁) and the impact (I) of the disease (breathlessness) in different clinical scenarios. EASI shows that the relationships between E, A, S and I vary markedly within individuals (through life) and between individuals (at the same age). It also helps to delineate some potentially relevant, but often overlooked concepts, such as disease progression, susceptibility to COPD and issues related to symptom perception.

In conclusion, EASI is an initial conceptual model to interpret the longitudinal and cross-sectional relationships between E, A, S and I in different clinical scenarios. Currently, it does not have any direct clinical application, thus it requires experimental validation and further mathematical development. However, it has the potential to open novel research and teaching alternatives.

Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

What does endotyping mean for treatment in chronic obstructive pulmonary disease?

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease, both at the clinical and biological level. However, COPD is still diagnosed and treated according to simple clinical measures (level of airflow limitation, symptoms, and frequency of previous exacerbations). To address this clinical and biological complexity and to move towards precision medicine in COPD, we need to integrate (bioinformatics) and interpret (clinical science) the vast amount of high-throughput information that existing technology provides (systems biology and network medicine) so diagnosis, stratification, and treatment of patients with COPD can occur on the basis of their pathobiological mechanism (ie, endotypes). Therefore, this Series paper discusses a possible new taxonomy of COPD, the role of endotypes and associated biomarkers and phenotypes, the gaps (and opportunities) in existing knowledge of COPD pathobiology, how systems biology and network medicine can improve understanding of the disease and help to identify relevant endotypes and their specific biomarkers, and how endotypes and their biomarkers can improve the precision, effectiveness, and safety of the treatment of patients with COPD.

Using omics approaches to understand pulmonary diseases

Omics approaches are high-throughput unbiased technologies that provide snapshots of various aspects of biological systems and include: 1) genomics, the measure of DNA variation; 2) transcriptomics, the measure of RNA expression; 3) epigenomics, the measure of DNA alterations not involving sequence variation that influence RNA expression; 4) proteomics, the measure of protein expression or its chemical modifications; and 5) metabolomics, the measure of metabolite levels. Our understanding of pulmonary diseases has increased as a result of applying these omics approaches to characterize patients, uncover mechanisms underlying drug responsiveness, and identify effects of environmental exposures and interventions. As more tissue- and cell-specific omics data is analyzed and integrated for diverse patients under various conditions, there will be increased identification of key mechanisms that underlie pulmonary biological processes, disease endotypes, and novel therapeutics that are efficacious in select individuals. We provide a synopsis of how omics approaches have advanced our understanding of asthma, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), idiopathic pulmonary fibrosis (IPF), and pulmonary arterial hypertension (PAH), and we highlight ongoing work that will facilitate pulmonary disease precision medicine.

Network Analysis of Lung Transcriptomics Reveals a Distinct B-Cell Signature in Emphysema

RATIONALE

Chronic obstructive pulmonary disease (COPD) is characterized by chronic airflow limitation caused by a combination of airways disease (bronchiolitis) and parenchymal destruction (emphysema), whose relative proportion varies from patient to patient.

OBJECTIVES

To explore and contrast the molecular pathogenesis of emphysema and bronchiolitis in COPD.

METHODS

We used network analysis of lung transcriptomics (Affymetrix arrays) in 70 former smokers with COPD to compare differential expression and gene coexpression in bronchiolitis and emphysema.

MEASUREMENTS AND MAIN RESULTS

We observed that in emphysema (but not in bronchiolitis) (1) up-regulated genes were enriched in ontologies related to B-cell homing and activation; (2) the immune coexpression network had a central core of B cell-related genes; (3) B-cell recruitment and immunoglobulin transcription genes (CXCL13, CCL19, and POU2AF1) correlated with emphysema severity; (4) there were lymphoid follicles (CD20(+)IgM(+)) with active B cells (phosphorylated nuclear factor-κB p65(+)), proliferation markers (Ki-67(+)), and class-switched B cells (IgG(+)); and (5) both TNFRSF17 mRNA and B cell-activating factor protein were up-regulated. These findings were by and large reproduced in a group of patients with incipient emphysema and when patients with emphysema were matched for the severity of airflow limitation of those with bronchiolitis.

CONCLUSIONS

Our study identifies enrichment in B cell-related genes in patients with COPD with emphysema that is absent in bronchiolitis. These observations contribute to a better understanding of COPD pathobiology and may open new therapeutic opportunities for patients with COPD.

Network science meets respiratory medicine for OSAS phenotyping and severity prediction

Obstructive sleep apnea syndrome (OSAS) is a common clinical condition. The way that OSAS risk factors associate and converge is not a random process. As such, defining OSAS phenotypes fosters personalized patient management and population screening. In this paper, we present a network-based observational, retrospective study on a cohort of 1,371 consecutive OSAS patients and 611 non-OSAS control patients in order to explore the risk factor associations and their correlation with OSAS comorbidities. To this end, we construct the Apnea Patients Network (APN) using patient compatibility relationships according to six objective parameters: age, gender, body mass index (BMI), blood pressure (BP), neck circumference (NC) and the Epworth sleepiness score (ESS). By running targeted network clustering algorithms, we identify eight patient phenotypes and corroborate them with the co-morbidity types. Also, by employing machine learning on the uncovered phenotypes, we derive a classification tree and introduce a computational framework which render the Sleep Apnea Syndrome Score (SAS_Score); our OSAS score is implemented as an easy-to-use, web-based computer program which requires less than one minute for processing one individual. Our evaluation, performed on a distinct validation database with 231 consecutive patients, reveals that OSAS prediction with SAS_Score has a significant specificity improvement (an increase of 234%) for only 8.2% sensitivity decrease in comparison with the state-of-the-art score STOP-BANG. The fact that SAS_Score has bigger specificity makes it appropriate for OSAS screening and risk prediction in big, general populations.

Treatable traits: toward precision medicine of chronic airway diseases

Asthma and chronic obstructive pulmonary disease (COPD) are two prevalent chronic airway diseases that have a high personal and social impact. They likely represent a continuum of different diseases that may share biological mechanisms (i.e. endotypes), and present similar clinical, functional, imaging and/or biological features that can be observed (i.e. phenotypes) which require individualised treatment. Precision medicine is defined as "treatments targeted to the needs of individual patients on the basis of genetic, biomarker, phenotypic, or psychosocial characteristics that distinguish a given patient from other patients with similar clinical presentations". In this Perspective, we propose a precision medicine strategy for chronic airway diseases in general, and asthma and COPD in particular.

LSC 2016: from system approaches in lung disease to getting the job you want

The Lung Science Conference (LSC) 2016 generated, as in previous years, fruitful discussions and exciting networking opportunities in the breathtaking venue of the Palace Hotel, Estoril, Portugal.

Highlights of the Lung Science Conference 2016http://ow.ly/4nsKG2

Molecular and clinical diseasome of comorbidities in exacerbated COPD patients

The frequent occurrence of comorbidities in patients with chronic obstructive pulmonary disease (COPD) suggests that they may share pathobiological processes and/or risk factors.To explore these possibilities we compared the clinical diseasome and the molecular diseasome of 5447 COPD patients hospitalised because of an exacerbation of the disease. The clinical diseasome is a network representation of the relationships between diseases, in which diseases are connected if they co-occur more than expected at random; in the molecular diseasome, diseases are linked if they share associated genes or interaction between proteins.The results showed that about half of the disease pairs identified in the clinical diseasome had a biological counterpart in the molecular diseasome, particularly those related to inflammation and vascular tone regulation. Interestingly, the clinical diseasome of these patients appears independent of age, cumulative smoking exposure or severity of airflow limitation.These results support the existence of shared molecular mechanisms among comorbidities in COPD.

The asthma-COPD overlap syndrome: towards a revised taxonomy of chronic airways diseases?

Most research of treatments for airways diseases has been restricted to patients who meet standard definitions of either chronic obstructive pulmonary disease (COPD) or asthma, yet to distinguish COPD from asthma in adult patients who have clinical features of both can be challenging. Treatment guidelines provide scant advice on how such patients should be managed. With increasing recognition that asthma and COPD are heterogeneous diseases, attention has been directed to the needs of a group of patients with what is now termed asthma-COPD overlap syndrome (ACOS), particularly in view of the high morbidity in this population. This Review considers the epidemiology, mechanisms of disease, current attempts to define and diagnose ACOS, existing and potential treatment options, and new approaches to the phenotyping and taxonomy of airway diseases.

COPD comorbidities network

Multimorbidity frequently affects the ageing population and their co-existence may not occur at random. Understanding their interactions and that with clinical variables could be important for disease screening and management.

In a cohort of 1969 chronic obstructive pulmonary disease (COPD) patients and 316 non-COPD controls, we applied a network-based analysis to explore the associations between multiple comorbidities. Clinical characteristics (age, degree of obstruction, walking, dyspnoea, body mass index) and 79 comorbidities were identified and their interrelationships quantified. Using network visualisation software, we represented each clinical variable and comorbidity as a node with linkages representing statistically significant associations.

The resulting COPD comorbidity network had 428, 357 or 265 linkages depending on the statistical threshold used (p≤0.01, p≤0.001 or p≤0.0001). There were more nodes and links in COPD compared with controls after adjusting for age, sex and number of subjects. In COPD, a subset of nodes had a larger number of linkages representing hubs. Four sub-networks or modules were identified using an inter-linkage affinity algorithm and their display provided meaningful interactions not discernible by univariate analysis.

COPD patients are affected by larger number of multiple interlinked morbidities which clustering pattern may suggest common pathobiological processes or be utilised for screening and/or therapeutic interventions.

Drug development for airway diseases: looking forward

Advancing drug development for airway diseases beyond the established mechanisms and symptomatic therapies requires redefining the classifications of airway diseases, considering systemic manifestations, developing new tools and encouraging collaborations.

Current concepts in targeting chronic obstructive pulmonary disease pharmacotherapy: making progress towards personalised management

Chronic obstructive pulmonary disease (COPD) is a common, complex, and heterogeneous disorder that is responsible for substantial and growing morbidity, mortality, and health-care expense worldwide. Of imperative importance to decipher the complexity of COPD is to identify groups of patients with similar clinical characteristics, prognosis, or therapeutic needs, the so-called clinical phenotypes. This strategy is logical for research but might be of little clinical value because clinical phenotypes can overlap in the same patient and the same clinical phenotype could result from different biological mechanisms. With the goal to match assessment with treatment choices, the latest iteration of guidelines from the Global Initiative for Chronic Obstructive Lung Disease reorganised treatment objectives into two categories: to improve symptoms (ie, dyspnoea and health status) and to decrease future risk (as predicted by forced expiratory volume in 1 s level and exacerbations history). This change thus moves treatment closer to individualised medicine with available bronchodilators and anti-inflammatory drugs. Yet, future treatment options are likely to include targeting endotypes that represent subtypes of patients defined by a distinct pathophysiological mechanism. Specific biomarkers of these endotypes would be particularly useful in clinical practice, especially in patients in which clinical phenotype alone is insufficient to identify the underlying endotype. A few series of potential COPD endotypes and biomarkers have been suggested. Empirical knowledge will be gained from proof-of-concept trials in COPD with emerging drugs that target specific inflammatory pathways. In every instance, specific endotype and biomarker efforts will probably be needed for the success of these trials, because the pathways are likely to be operative in only a subset of patients. Network analysis of human diseases offers the possibility to improve understanding of disease pathobiological complexity and to help with the development of new treatment alternatives and, importantly, a reclassification of complex diseases. All these developments should pave the way towards personalised treatment of patients with COPD in the clinic.

Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014

This Pulmonary Perspective summarizes the content and main conclusions of an international workshop on personalized respiratory medicine coorganized by the Barcelona Respiratory Network ( www.brn.cat ) and the AJRCCM in June 2014. It discusses (1) its definition and historical, social, legal, and ethical aspects; (2) the view from different disciplines, including basic science, epidemiology, bioinformatics, and network/systems medicine; (3) the bottlenecks and opportunities identified by some currently ongoing projects; and (4) the implications for the individual, the healthcare system and the pharmaceutical industry. The authors hope that, although it is not a systematic review on the subject, this document can be a useful reference for researchers, clinicians, healthcare managers, policy-makers, and industry parties interested in personalized respiratory medicine.