COPD Biomarkers and their interpretation; a Personal

perspective

Robert A Stockley1, David Halpin2, Bartolome R Celli3 and

Dave Singh4.

Affiliations

1.Lung Investigation Unit, Medicine - University Hospitals Birmingham NHS Foundation

Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15

2GW,UK. 2. Department of Respiratory Medicine, Royal Devon & Exeter Hospital, Barrack

Road, Exeter, EX2 5DW, UK. 3.Pulmonary and Critical Care Dept. Brigham and Women’s

Hospital, Harvard Medical School, Boston, MA,USA. 4. University of Manchester, Medicines

Evaluation Unit, Manchester University NHS Foundation Hospital Trust, Southmoor Road,

Manchester, M23 9QZ, UK.

Corresponding Author

Robert A Stockley Lung Investigation Unit, Medicine - University Hospitals Birmingham NHS

Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston,

Birmingham, B15. Telephone +44 121 3716808: E mail r.a.stockley@bham.ac.uk

Authors contribution

RAS conceived of the project and wrote the initial draft. DH BC and DS critically appraised

and added to the content and approved the final submission.

Running head. COPD Biomarkers

Descriptor COPD Emphysema

Total word count 5083.

At a Glance

The pathology and impact of COPD results from an abnormal inflammatory process

resulting in tissue damage with ineffective repair in response to toxic inhalants (especially

cigarette smoke). Identification of mechanisms provides the opportunity to develop new

therapies and a personalised approach to management. The collection of multiple genetic

and detailed biochemical data from small and large patient cohorts has led to an explosion

of studies investigating biomarkers, to achieve these aims.

Despite widespread enthusiasm and many statistically significant associations, the

interpretation of COPD biomarker results requires thought and leaves many questions

unanswered. The current review assesses the importance of these associations, whether

they represent cause or effect, reflect disease severity or activity, the complexity of the

pathway to the final pathogenic and hence interventional step, and problems interpreting

cross sectional studies without knowing individual disease trajectories. The complexity of

biomarker specificity without sufficient clinical phenotype and endotype information

contributes to problems of interpretation. A strategic change is needed to develop useful

COPD biomarkers; this includes focusing on endotype biomarkers within specific clinical

phenotypes, biomarkers in the early phases of COPD development that may differ from

those in established disease , exacerbation subtype biomarkers, and biomarkers to predict

or measure drug effects. Generating these data are critical for the future and multiple

potential use/s of biomarkers. At present only blood eosinophils is close to implementation

in clinical practice.

Introduction

Biomarkers are defined as “characteristics that are objectively measured and evaluated as

an indicator of normal biological processes, pathogenic processes, or pharmacologic

responses to a therapeutic intervention” 1 and are increasingly explored in COPD for several

important reasons. These include diagnosis, facilitating patient characterisation, quantifying

disease severity, determining prognosis, predicting and detecting response to treatment as

well as investigating the pathophysiology of the disease/diseases 1. Clinical phenotyping

categorises patients using observable characteristics 2, while endotyping uses the presence

of a biological mechanism to define a patient subgroup2. The development of biomarkers

that identify endotypes most likely to respond to targeted pharmacological treatments is

integral to precision medicine2,3.

COPD is the result of an abnormal and persistent inflammatory process that damages the

lung architecture, resulting in progressive physiological deterioration (at least in some

patients)4. Understanding the processes involved can lead to the rational development of

new molecular biomarkers which can be used to (a) monitor disease ACTIVITY to identify

individuals at high risk of rapid disease progression (b) identify endotypes most likely to

respond to a pharmacological intervention and (c) measure the pharmacological modulation

of disease pathophysiology in clinical trials, particularly early phase 1/2 studies where proof

of pharmacological effect provides confidence to progress to larger phase 3 studies focused

on clinical efficacy. Biomarker development, validation and interpretation varies according

to the potential future clinical application as a biomarker of disease activity, endotype or

pharmacological effect.

COPD biomarker studies have investigated various lung media including sputum,

bronchoalveolar lavage and exhaled breath condensate5. All have major problems of

reproducibility1,6, and may rely on assays not developed for / validated in the medium being

studied. For practical purposes, most of the published studies have involved blood

biomarkers because of easy access and assay reproducibility, and therefore the current

article focusses primarily on blood biomarkers. It is not the purpose of the current article to

document the extensive COPD biomarker literature, but rather highlight problems that

complicate data interpretation. We also propose that the historic approach to biomarker

identification and validation needs to be modified, due to the clinical and biological

complexity of COPD. More careful consideration should be given to the study designs

required to develop biomarkers that measure disease activity, identify endotypes (that

might predict response to treatment) or monitor response to pharmacological treatment.

COPD vs controls.

Many COPD biomarker studies have evaluated disease versus healthy controls in the hope

of finding a difference between groups coupled with little or no overlap between group

individual measurements. As COPD is an inflammatory disease it is understandable that

many markers such as CRP, TNFα, IL6, IL8, fibrinogen, neutrophil numbers and activation are

raised in COPD patients7,8. While statistically significant differences between groups for such

biomarkers have been reported once all available confounding factors have been taken into

account, the individual subjects usually show marked overlap that reduces both the

sensitivity and specificity of the biomarker as a diagnostic tool for COPD. This could reflect

time dependant variations in disease activity in only a proportion of patients or different

clinical phenotypes as well as assay variability, particularly in individuals with low grade

inflammation where the disease noise to signal ratio may be difficult to distinguish . This

uncertainty can only be addressed by assay characterisation, extensive phenotyping, and/or

longitudinal follow up.

The analysis of plasma cytokine and chemokine biomarkers in the COPD gene (n=2123) and

SPIROMICS (n=1117) cohorts7 illustrates these problems; the data ranges for COPD patients

in both cohorts were wide with a large overlap between COPD patients and controls.

Significant p values were obtained for comparisons between groups for some biomarkers

such as eotaxin and IL-6, but the considerable overlap prevents use as a diagnostic

biomarker in clinical practice. In addition, even though the systemic levels of biomarkers

may relate to outcomes, they provide little prognostic value above easily obtainable clinical

variables9.

In smokers with symptoms, the diagnosis of COPD is based on the FEV 1/FVC ratio of ≤0.7 as

recommended by GOLD10. This has been frequently challenged as this cut-off is too low in

younger adults and too high in more elderly subjects. This becomes most important in the

presence of a wide range of values for those classified as “normal”, especially in subjects

with “early” phases of disease when significant pathological change can exist in the small

airways prior to influencing forced expiratory values detected by spirometry11. In addition,

because of the wide normal range, subjects can have either excessive or minimal

physiological decline before the “disease threshold” is crossed12 giving a false impression of

disease ACTIVITY. Furthermore, recent studies have also identified subjects with regular

symptoms and periodic worsenings that resemble COPD exacerbations and those with

significant emphysema on CT scans who do not meet the spirometric criterion for COPD

diagnosis13,14. A proposal to identify this ‘early phase of COPD’ in younger (<50 years age)

individuals with normal FEV1, has suggested the criteria required; FEV1/FVC ratio <0.7 or CT

scan evidence of emphysema or documented rapid FEV1 decline15. This proposal moves

beyond the usual spirometric criteria in order to identify individuals with high levels of

disease activity at a relatively early age that precedes crossing the spirometric threshold.

While these criteria need validation, biomarker studies in such individuals that focus on

disease ACTIVITY prior to the development of significant tissue damage would support early

effective clinical management and indeed may differ from the activity processes as the

disease becomes more established (see later).

Cross-sectional studies; Cause or effect

Cross-sectional studies have frequently been used to assess the association between

biomarkers and COPD severity, usually expressed as the FEV1 percent predicted. A

statistically significant correlation is usually taken as a positive finding, but then raises the

question of “cause or effect”. The biomarker may reflect a pathophysiological process that

drives or fails to dampen the ongoing development of lung damage (“cause”), and therefore

be related to disease activity levels. For this to be a distinct possibility all subjects studied

should be age matched suggesting they have reached different degrees of severity in the

same time frame. For the same reason, all should have similar smoking history and

treatment. However, the relationship may still reflect disease severity (“effect”) as the

biomarker is measuring lung damage itself either directly or indirectly, or may be an

epiphenomenon reflecting a shared aetiological factor, or even be a cohort related chance

finding.

The use of the statistical p value will determine the likelihood of a type 2 error (chance

finding) of the study. However, it has been suggested that such analyses should use a lower

threshold p value to provide additional reassurance that the findings do not simply reflect

chance16 especially when multiple marker analysis is undertaken. In addition the larger the

sample size the more likely that minimally important differences achieve statistical support.

Also important is the strength of the association (rho value), as statistically significant

associations (judged by p value, usually set at 0.05 and lower) with a low rho value are likely

to have little overall clinical implication (see below). For these reasons such correlations

need careful analysis and interpretation.

Temporal associations may help interpretation with careful matching of lung function and

age. At the same level of lung function impairment, in general, those with a rapid

progression/higher disease activity should be younger than those with slow progression,

assuming the disease started at a similar age. Similarly with patients matched for age those

with rapid progression/higher disease activity should have worse lung function, again

assuming the disease started at a similar age. The interpretation of cross-sectional data

from cohorts based on levels of lung function impairment even when matched for age is

further complicated by the observation that the normal range is wide and in some people

low levels of baseline lung function result from poor lung development in early life 4. Indeed,

the diagnosis of COPD in some individuals may be mainly attributable to poor lung

development, associated only with a relatively slow subsequent rate of FEV1 decline in later

life. None of this can be understood from cross-sectional data, as different levels of disease

ACTIVITY can be present in individuals with the same FEV1 percent predicted, even after

careful matching for age and smoking history as the starting point is unknown. Ideally,

longitudinal data-collection is needed to determine on going progression rate and properly

develop biomarkers that reflect disease ACTIVITY.

The study by Campos and colleagues 17 in patients with alpha-1 antitrypsin deficiency (AATD)

illustrates these problems. Fast or slow FEV1 decliners were identified by studying different

age cohorts matched for smoking history and FEV1. The oldest cohort was therefore defined

as a group with “slower” decline in lung function compared to the younger cohorts,

consistent with differences in long term disease ACTIVITY. However, there are 2 alternative

explanations; firstly that each cohort had progressed at the same rate but the older cohort

started at a higher baseline lung function than the younger cohort. Secondly, and potentially

more likely, each cohort had developed a similar decline until they stopped smoking (all

groups stopped in their 40s) and thereafter disease progression stabilised. Without baseline

lung function and longitudinal FEV1 measurements, the true explanation remains uncertain.

This is consistent with recent longitudinal data in AATD where ex and never smokers had a

similar rate of decline despite differing lung function at baseline 18 . In this scenario, any

biomarker associated with lung function using a simple cross-sectional analysis may reflect

disease SEVERITY rather than ACTIVITY (Figure2). Longitudinal analysis with careful

consideration of smoking history is the only way to overcome this conundrum.

Figure1 The figure demonstrates the path of idealised FEV1 decline with time, derived from

longitudinal progression in AATD 17. If the factors responsible for the decline persist, the

cross sectional assessment at the age of 60 will reflect the ongoing cause (ACTIVITY marker).

However if the progression has been amplified by smoking (the dotted line) cessation of

smoking will return the disease progression to its natural course. In this instance any marker

of disease ACTIVITY will no longer relate cross sectionally to the SEVERITY at the age of 60

and could be dismissed as irrelevant. The non-smoking and smoking pathological process

can therefore only be explored prospectively in cohorts of healthy never smokers, current or

ex-smokers with and without COPD.

Longitudinal studies of disease progression

The rate of progression of general COPD and that due to AATD is highly variable 4,18-20. The

term “rapid” decliners is often used, although a standard definition is lacking because lung

function normally declines with age and depends on height. Perhaps it is best to define

decline of FEV1 as % predicted to overcome this natural deterioration.

Linking baseline biomarkers to the pathophysiological processes causing lung damage

requires the concept that this is a continuous and homogenous process. This may be true, at

least for some clinical phenotypes and thus the baseline marker predicts this low grade

activity and hence subsequent progression regardless of whether the marker relates cross

sectionally to SEVERITY. Since many other factors can also influence decline (such as

smoking status, exacerbations and emphysema phenotype etc.20), it appears unlikely that a

single biomarker would identify all of the mechanisms associated with these different

factors21. Multiple biomarkers will likely be needed to identify these different disease

ACTIVITY mechanisms (endotypes) within well-defined and monitored clinical phenotypes.

In recent years the ability to make multiple measurements in cohort studies has become

possible by the “omics” explosion and multiplex assays. The relationship between plasma

cytokine and chemokine biomarkers and clinical parameters or outcomes including severity

of airflow obstruction, FEV1 decline, emphysema progression and exacerbations was studied

in the COPDgene and SPIROMICS cohorts7. However, a lack of replication between cohorts

was observed, and positive findings only gave minor additional value to clinical variables.

The heterogeneous clinical and pathophysiological nature of COPD likely explains some of

the discordance.

Various biomarkers have been studied in several COPD cohorts and the reported

relationship to patient outcomes are summarised and appraised for the most frequently

studied biomarkers in table 1.8 These include surfactant protein D (SP-D) and club cell

protein 16 (CC-16). SP-D is involved in pulmonary innate immune defence, while CC-16 is an

immunosuppressant protein secreted by Clara cells that protects against excessive lung

injury22. Increased blood SP-D levels and decreased CC-16 levels were reported in COPD

patients compared to controls8,23, although in both cases there was a large data overlap

between the groups. SP-D levels were statistically associated with emphysema progression

and mortality over 3 years24, while low CC16 levels were related to FEV1 decline, but not

mortality. The findings for SP-D were also seen in COPDgene but the overlap and influence

of other factors on SP-D levels including age, BMI, gender and current smoking makes it

difficult to use on an individual basis for any treatment or prognostic outcome22.

A major study of over 4000 subjects over 10 years also analysed both the correlation of CC-

16 with lung function and its progression25. The statistical p values confirmed that the

relationships were highly unlikely to have occurred by chance and the authors concluded

that the measurement was informative of the pathophysiological process and could be used

to identify rapid decliners for future interventional studies of potential disease modifying

therapies. However, examination of the individual data (Figure 3) indicates the wide range

of values for the patient group. This is reflected in the r value for the correlation which,

though significant, is low. Indeed here the r2 value is helpful in that it indicates that less than

5% of the decline variability can be predicted in a given patient by measuring CC16. This

marker would therefore be of little use in enriching populations for studies of rapid

decliners or targeting patients with disease modifying therapy. Whether CC16 would be

more discerning in patients characterised for COPD features other than FEV1 remains to be

determined. The future development of biomarkers of disease progression will be improved

by studying distinct clinical subgroups (e.g. frequent exacerbators, emphysema etc).

Figure 2. The rate of decline in FEV1 is shown compared to baseline CC16 level. The relationship was statistically significant as shown by the p value and the linear relationship is shown by the regression line with its 95% confidence interval. However the scatter of the data is shown and for 2 patients highlighted by the circles with the same baseline level of CC16, subsequent decline is clearly vastly different emphasising it is not a suitable biomarker for predicting FEV1 decline or selection of rapid decliners for future studies despite the p value. Park HY, Churg A, Wright JL, et al. Club cell protein 16 and disease progression in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2013; 188(12): 1413-9. Figure 2 Reprinted with permission of the American Thoracic Society. Copyright © 2018 American Thoracic Society.

This concept of the variability of the relationship between biomarker and outcome such as

the FEV1 is important to note even when statistically significant. Where such data can be

derived the correlations between FEV1 and individual biomarkers indicate that little of the

variance (<1% to a maximum of 6%) can be attributed to the value 7,8,23. The use of multiple

biomarkers may improve the predictive value, though in general not greatly. However, a

combination of CC16, SP-D, CRP, sRAGE, and fibrinogen increases the sensitivity for FEV 1

cross sectionally to about 24% and combining SP-D, CRP, sRAGE and fibrinogen increases the

sensitivity for emphysema to approximately 30%26.

Emphysema; complexity of biomarker development

The role of neutrophil elastase (NE) has long had a prominent role in the pathophysiology of

COPD in general and emphysema in particular. AAT is the major serum inhibitor of serine

proteinases and especially NE. AAT deficiency impairs the ability to inhibit NE and since this

enzyme can cause all of the pathological features of COPD 27, it seems a clear direct path

from cause to effect consistent with susceptibility of patients with genetic deficiency to

develop emphysema even in the absence of smoking. NE is a potent elastolytic enzyme and

destruction28 or failure of elastin repair29 is certainly associated with emphysema like

changes in animal models. However, other potential elastolytic enzymes such as the

MMPs30 and cysteine proteinases31 have also been implicated via animal models suggesting

NE is not necessarily the sole and key mediator of the degradation of lung elastin. Increases

in the presence of these enzymes in COPD32,33 adds weight to their potential role especially

as these other enzyme classes also inactivate AAT function, thereby potentially facilitating

NE activity indirectly. On the other hand NE itself activates MMPs and cysteine proteinases

by cleavage of the pro-enzymes and inactivates their cognate inhibitors34. This leads to an

interactive cycle where the role of any enzyme group could be the initial step towards tissue

damage and alternative pathways may determine the eventual pathology (Figure 3). Both

pathways have complex interactions which lead to emphysema11,35. The interpretation of

biomarkers which are part of a pathophysiological network, rather than a distinct linear

chain is therefore complex as homeostatic mechanisms in a network may counteract

changes in a single biomarker and the relationship between that biomarker and disease

activity or tissue damage may or may not be modified.

Figure 3. The direct pathway of Neutrophil Elastase (NE) to Tissue damage leading to

Emphysema has long been described. At the same time NE activates the proenzymes of

Cathepsin B and MMPs which may also contribute to tissue damage.NE also inactivates the

inhibitors of Cathepsins and MMPs to facilitate activity of these groups of enzymes indirectly

leading to tissue damage whilst also damaging the inactivators of NE itself enhancing the

direct NE pathway. Emphysema leads to airflow obstruction (AO) reflected in the FEV1.

However there is also the alternative pathway involving the activation of growth factors by

MMPs leading to small airways disease (SAD) and hence AO by a different pathological

process35. Thus the biomarkers central to the disease process may also change as the

pathological phases of the disease change.

Measuring these enzymes in lung secretions may provide information of their presence in

the airway and role in epithelial damage, but not necessarily of their role in the interstitium

(where elastin degradation is thought to be central to the development of emphysema) or

fibrotic changes leading to loss of the small airways. Even measuring elastin breakdown and

its products in the circulation does not provide lung specific evidence of the enzyme /

enzymes involved, as skin36 and the cardiovascular system37 are major repositories for

elastin and degradation products may simply reflect cell degranulation/turnover or aging at

these sites and involve multiple enzymes and pathways not reflective of pulmonary tissue

breakdown.

At the point of release NE concentration exceeds the normal AAT concentration resulting in

an area of obligate proteolysis before dilution by diffusion to enable total enzyme

inhibition38. This area of obligate proteolysis is understandably greater in AATD39. This

means that migrating neutrophils produce protein degradation in the immediate vicinity

that can be tracked by specific cleavage products such as that for fibrinogen40 or elastin41.

These biomarkers may provide more direct evidence of enzyme activity in the lung (or other

tissues with ongoing neutrophilic inflammation) and hence reflect the immediate efficacy of

specific NE inhibitors. Indeed the fibrinogen elastase biomarker decreases in AATD patients

on AAT augmentation therapy40. The development of such markers specific for the

emphysema phenotype of COPD may provide information on disease ACTIVITY and also

indicate early response to therapeutic intervention.

Soluble receptror for advanced glycation end-products (sRAGE); Plausible or not ?

sRAGE is a biomarker that demonstrates pathophysiologic credibility, genetic associations

and a relationship to emphysema. Inflammatory mediators associated with COPD and

emphysema generate RAGE ligands, which interact with cell membrane RAGE, activating

intracellular pathways leading to gene transcription of many inflammatory proteins which

then interact with RAGE forming an amplification loop. RAGE cleavage by

metalloproteinases release soluble RAGE (sRAGE) which acts as a decoy interrupting cell

RAGE binding by its ligands and reduces cell associated RAGE receptors, both with putative

beneficial effects on the inflammatory cascade. RAGE overexpression in mice increases

susceptibility to developing air space enlargement42 and KO mice have reduced

inflammation in response to cigarette smoke43. Furthermore, wild type mice treated with

sRAGE display less inflammation as it acts as a decoy receptor44.

Human studies have demonstrated a relationship of COPD and emphysema to blood sRAGE

levels (Table 1). There are lower sRAGE levels in patients with emphysema compared to

controls45, and the level of sRAGE relates to GOLD severity stage46,47. Cheng et al48

demonstrated that lower sRAGE levels related to both gas transfer and lung density (as a

marker of emphysema), and a SNP was identified that was related to sRAGE levels. Iwanato

and colleagues demonstrated lower sRAGE levels in COPD and a relationship to lung

function decline49.

Superficially these data indicate that not only is sRAGE a viable mediator of the

pathophysiological processes implicated in COPD but also a marker of disease ACTIVITY and

likely has a genetic cause. However, careful examination uncovers uncertainties in the

findings. Firstly the individual values in COPD are wide with significant overlap with controls.

Only Cockayne et al46 showed all the individual data points (rather than mean / SD or

median / interquartile range), and only a minority of COPD patients were different to

controls even though the group data is statistically different. The relationship to gas

transfer accounted for up to 21% of the variability. Iwanato and colleagues 49 confirmed the

wide range of sRAGE values, but also showed that levels for both smoking controls and

smokers with COPD were lower compared to non-smokers. The similarity between both

smoking groups suggested that sRAGE is modulated by smoking and not COPD specific.

Baseline sRAGE levels did not predict FEV1 decline, although in a multivariate model both

current smoking and sRAGE levels were independent predictors of the rate of FEV1/FVC ratio

decline.

Where does this data leave us? Although sRAGE remains the best associated marker of the

emphysema phenotype of COPD24 the associations are mainly weak (though statistically

significant). It remains to be seen how sRAGE responds to factors that slow or stop

emphysema progression (such as smoking cessation). sRAGE regulation by other

inflammatory processes in COPD such as exacerbations, bacterial colonisation and

bronchiectasis should be studied to understand if this represents a physiological response to

inflammatory processes in general. The impact of the genetic polymorphism also need to

be determined. So even this biomarker has a long way to go before it can be used to

understand the pathophysiology or as part of a precision medicine approach.

Exacerbation Biomarkers

Multiplex methodology was used to evaluate the potential of 90 protein biomarkers to

predict exacerbation frequency in the COPDgene (n=1544) and SPIROMICS (n=602)

cohorts50. Although biomarkers predictive of exacerbations were found in each cohort, there

was poor replication between the studies. This likely reflects the fact that COPD

exacerbations have many causes and the definition of an exacerbation remains the

“subjective” clinical assessment of the need for increased pharmacological treatment, which

adds to variability. Given the heterogeneous nature of exacerbations, it is unlikely that one

biomarker could reliably predict such a range of clinical events better than past history. The

blood biomarkers studied in the COPDgene and SPIROMICS cohorts provided only a

marginal additional improvement to the ability of clinical characteristics to predict

exacerbations50. A combined analysis of 5 cohorts including the ECLIPSE cohort showed that

plasma fibrinogen used with a clinical history of exacerbations increased the ability to

predict future events51, leading to FDA qualification of this biomarker in the context of use

as an enrichment tool for recruitment into clinical trials with exacerbations as an outcome.

However, the additional value of adding plasma fibrinogen in the context of clinical trial use

is relatively small and lacks the sensitivity and specificity to be used on an individual basis in

clinical practice50. Furthermore, fibrinogen failed to emerge in the COPDgene and

SPIROMICS cohorts, albeit using a different analytical method, which highlights the general

need to also standardise biomarker analytical methodology. Ultimately, the utility of

predictive exacerbation biomarkers will only be improved by studying exacerbation

subtypes defined by clinical criteria combined with objective biological criteria.

Pharmacological biomarkers

There is increasing interest in whether biomarkers which reflect underlying

pathophysiological processes can be used to predict the effects of therapeutic interventions.

There are many novel immunomodulatory drugs in clinical development that selectively

target components of the host immune system, and are unlikely to be effective in all COPD

patients. Combining clinical information with biomarkers to improve the benefit versus risk

ratio is essential for the future clinical use of these drugs.

There is growing evidence that blood eosinophil counts can be used to predict the effects of

certain drug classes in COPD patients. A subgroup of COPD patients have increased

eosinophil numbers in the lungs52, and there is a relationship between blood and lung

eosinophil numbers52-54. Much attention has been focused on the relationship between

blood eosinophil counts and exacerbation risk, which has been found to be positive in some

COPD studies, but not others55-57. The reasons for negative findings may reflect the inclusion

of patients with no exacerbation history, the confounding effects of concurrent ICS use and

insufficient sample size; the larger COPDgene and ECLIPSE cohorts have shown blood

eosinophil counts add predictive value in the subgroup of individuals with a history of

exacerbations57. Regardless, the main interest in blood eosinophil counts has arisen from

post-hoc analyses of clinical trials comparing ICS/LABA versus LABA indicating that blood

eosinophils predict the ICS effect on exacerbation prevention, with a continuous “blood

eosinophil concentration - response” relationship and a treatment effect of approximately

50% observed at > 300 cells/µL58,59. A large post-hoc analysis of 3 clinical trials (n=4528)

comparing ICS/LABA with LABA showed a significant effect of ICS at > 100 cells/µL, with the

effect size increasing with higher blood eosinophil counts60. The predictive nature of blood

eosinophils with regard to ICS effects has now been confirmed in prospective clinical trials61-

63.

The predictive ability of blood eosinophils with regards to ICS response is not an “all or

nothing” phenomenon64, and a single dichotomous threshold does not fully describe this

relationship. Nevertheless, the threshold of approximately 100 cells/µL is emerging as a

negative predictor for ICS response, and could be used as part of a precision medicine

strategy to optimise the benefit versus risk of ICS use at an individual level. There seem to

be underlying biological differences between the airways of eosinophilic and non-

eosinophilic COPD patients with the former having higher levels of T2 cytokines and greater

reticular basement membrane thickening54,65, drawing a similarity to airway inflammation in

asthma. Furthermore, there is less airway colonisation with pathogenic bacteria in

eosinophilic COPD54. These biological differences could explain the variations in ICS

response.

Blood eosinophil counts seem to predict the clinical response to monoclonal antibody

targeting of the cytokine IL-5 which lowers eosinophil counts 66, and post-hoc analysis of

clinical trials of the PDE4 inhibitor roflumilast suggest that patients with higher blood

eosinophil counts experience a greater clinical benefit67. Interestingly, in the mepoluzimab

trial, reduction of exacerbations were minimal compared with placebo, even though the

blood eosinophil counts were reduced to less than 50 cells/µl for the 1 year duration of the

trial. The use of blood eosinophil counts may be affected by the reproducibility of the

measurement56,68, although this appears to be more stable at lower levels69. It should be

noted that this measurement is reduced during sepsis and by oral corticosteroid use52.

Understanding biomarker data

Studies of biomarkers will continue to be published especially when statistically significant

data is reported suggesting it is unlikely to reflect a type 2 error. Whereas the p value

indicates the likelihood of an association having occurred by chance its threshold needs

adjustment when multiple assays are undertaken and the separation of data from

appropriate controls providing sensitivity and specificity as well as positive (or negative)

predictive values and the r2 value of correlations will indicate the strength of the message.

Studies with multiple biomarkers are suitable for a systems biology approach to gain a

better understanding of the interactions of components into defined networks; this may

offer a new dimension to the reductionist approach to understanding pathophysiology.

Whether this results in the emergence of more or less potential biomarkers or realistic

targets for drug development remains to be seen. In terms of a practical biomarker for daily

use, the centre of the network may prove less important than downstream components.

The development of new COPD biomarkers and interpretation of data therefore requires

more extensive study and careful interpretation than is currently undertaken. We

summarise key issues below, and propose areas of importance to focus on regarding the

further development of COPD biomarkers

1.Comparisons of COPD versus controls: Given the inevitable overlap between COPD and

controls, it is important to perform comprehensive clinical characterisation with multiple

groups including matched healthy smokers and ex smokers, with current and ex smoking

COPD patients. In addition it is important to understand if the outliers are a distinct clinical

phenotype and / or whether specific biological mechanisms reflecting disease activity are

the cause. The investigation of younger patients with “early stages of COPD” is important in

this context.

2.Endotype biomarker: Dissecting COPD into component mechanisms is essential for the

development of biomarkers. Endotype biomarkers that reflect these mechanisms may have

multiple applications in COPD, ranging from monitoring disease activity to identifying

individuals likely to respond to a therapeutic intervention or measuring the pharmacological

effects of drugs. AAT deficiency is an example of a COPD endotype that is detected by a

specific biomarker. A number of endotypes have been proposed (T2/ eosinophilic COPD,

bacterial colonisation, systemic inflammation)2; further biomarker studies of these

endotypes should be carried out in specific clinical phenotypes (e.g. frequent exacerbators,

rapid decliners) rather than poorly characterised and broad populations.

3.COPD ACTIVITY: The discovery of biomarkers related to the process/es that cause direct

damage or block repair can be used to monitor disease activity, particularly after

therapeutic interventions. Longitudinal studies in discrete clinical phenotypes, rather than

general COPD populations, are needed to identify disease activity biomarkers correctly

related to specific mechanisms. The influence of factors such as active smoking need to be

taken into account, and changes in such biomarkers over time in both stable and

progressive disease need to be determined. The biomarker needs to have or reflect a

plausible mechanism of progression.

4.Pharmacological biomarker: Precision medicine involves the use of clinical and biological

information to predict therapeutic response. The use of blood eosinophil counts in COPD

patients at high risk of exacerbation to predict ICS effect is an example of such an approach.

Biomarkers may also be used to measure drug responses, and it should be determined

whether it is robust enough to be used clinically in an individual patient and / or as an

endpoint especially in early phase clinical trials.

5.Exacerbation biomarkers: Biomarkers should be developed that add extra information in

addition to the history of exacerbations, such as the cause/nature of the exacerbation/s and

the need/likely response to specific therapies.

6. Assay validation. Many studies include the use of multiple assay systems not necessarily

developed in the biological medium to be assessed. This together with the assay variability,

lower limit of detection and lower limit of quantification are essential information and

critical in the assessment of low grade inflammation in COPD patients without

exacerbations. Such information would be enhanced by day to day variation in the values

obtained as well as monthly or annual confirmation of stability to explore relationships with

long term disease progression.

Conclusion

Future biomarker studies need to move away from the simplistic approach of comparing

COPD patients versus controls, for all the reasons already outlined, and focus on more

targeted questions in specific patient subgroups / phenotypes. The concepts of disease

endotypes and disease activity are crucial to this future approach. The positive data

regarding the ability of blood eosinophil counts (a biomarker of an endotype) in patients at

increased exacerbation risk (clinical phenotype) to predict response to ICS integrates some

of these concepts, providing an opportunity to use this biomarker to practice precision

medicine in COPD patients. We advocate more focussed and detailed studies in the future

or we run the continued risk of data overload and missing the initiating and mediating

factors responsible required for new therapy design and delivery in COPD.

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Table 1

Biomarker FEV1 FEV1

declineEmphysema Emphysema

progressionMortality Comment

34% 53% 49% 31% Variance explained by using clinical markers

1.CC16 2% 3% 5% 0.5% 2% StableAnti-inflammatory Marked variance

2.SP-D 3% 8% 4% 0.8% 3% StableAnti-inflammatoryAnti-oxidant

3.sRAGE 10% 4% 18% 4% 3% Stability: UnknownInflammation decoy

4.CRP 9% 0% 4% 0.6% 1% UnstableNon-specific acute phase response

5.Fibrinogen 11% 2% 13% 0.6% 2% UnstableCoagulation Acute phase response

6.IL-6 4-5% low UnstablePro and anti-inflammatory.Acute phase response. Chemokine signalling but complete overlap in COPD

Combinedmarkers

1-524%

1,3,57%

2-530%

1,3,54.3%

2,4,56%

The clinical outcomes are shown together with selected biomarkers numbered 1 to 6. The variance of the outcome explained by the biomarker is shown as a % and combination of biomarkers are shown in red in each column. Data is summarised where available from the ECLIPSE study and references 6, 7 and 26.

The conclusion from Reference 26 was “Therefore, the field must acknowledge that statistically significant associations between biomarkers and outcomes that can be observed in large cohorts may be largely inadequate to explain remaining variance after strong clinical covariates are included in the models.”