WO2017158152A1

WO2017158152A1 - Diagnosis of chronic obstructive pulmonary disease (copd)

Info

Publication number: WO2017158152A1
Application number: PCT/EP2017/056362
Authority: WO
Inventors: Tilo Buschmann; Sabina CHRIST-BREULMANN; Maik FRIEDRICH; Jens HOHLFELD; Friedemann Horn; Norbert Krug; Kristin Reiche; Kai Sohn
Original assignee: Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V.
Priority date: 2016-03-17
Filing date: 2017-03-17
Publication date: 2017-09-21

Abstract

The invention relates to a method for the diagnosis of COPD, risk stratification, disease outcome, disease prognosis or differential severity analysis of COPD in a subject that has or is suspected to have COPD, comprising the steps of, in a sample of said subject: (a) determining the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof in a sample taken from said subject, wherein the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is correlated with the diagnosis of COPD, a defined risk stratification, a defined disease outcome, a defined disease prognosis, or a differential severity of COPD in a subject, wherein said diagnosis of COPD, defined risk stratification, defined disease outcome, defined disease prognosis, or a differential severity of COPD in a subject is given if the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is above or below a certain cut-off value.

Description

Diagnosis of chronic obstructive pulmonary disease (COPD)

Field of the invention

The present invention is in the field of medicine, more in particular diagnostics and even more in particular the diagnosis and prognosis of chronic obstructive pulmonary disease (COPD), a chronic airway inflammation associated with cough, sputum, and shortness of breath. Background

Chronic obstructive pulmonary disease (COPD), a chronic airway inflammation associated with cough, sputum, and shortness of breath, is characterized by airflow limitation and a progressive decline in lung function. Besides the lung as the target organ for COPD risk factors, such as tobacco smoke, other organs are affected leading to comorbidities contributing to the morbidity and mortality of the disease. Being the fourth leading cause of death at present, it is predicted that COPD will rank third in global mortality by 2020. COPD is a widespread disease that affects 600 million people worldwide. In Germany, nearly 15% of the over 40-year-old population suffer from COPD. The prevalence and burden of COPD is projected to increase worldwide in the coming decades because of continued exposure to COPD risk factors and the changing age structure of the population due to increasing longevity. Over decades, men spearheaded the statistics on COPD. Today, almost as many women as men suffer from COPD, due to changes in smoking behavior of women and their increased susceptibility to pollutants in cigarette smoke. Importantly, cigarette smoking does not inevitably lead to development of COPD. Rather, it depends on the individual susceptibility to develop COPD upon chronic exposure to pollutants.

It is important to improve the diagnostic armamentarium for COPD due to its high prevalence, broad differences in individual susceptibility, and progressive clinical course leading to morbidity and mortality with huge socioeconomic impact. While pulmonary function as a physiology measure is the current standard for diagnosis, staging, and assessment of treatment response, there are no blood-based biological biomarkers that reliably contribute to clinical decision making.

COPD treatment is mainly symptomatic targeting the airway obstruction with short- and long-acting bronchodilators. Here, measurement of pulmonary function is the key outcome variable to monitor treatment responses. Treatment regimens are based on disease severity with pulmonary function guiding the standard of care. While assessment of treatment responses by pulmonary function measurement is standard of care for bronchodilator therapy because it directly captures changes in airway physiology, the development of novel anti-inflammatory drugs is hampered due to the indirect connection of airway physiology with the underlying airway inflammation. While it is very well conceivable that anti-inflammatory treatments ultimately affect and improve airway function, it would be desirable to directly monitor treatment responses with a valid biomarker that indicates anti-inflammatory efficacy positively impacting on morbidity and mortality. Measurement of pulmonary function is the current standard of care to monitor treatment responses in COPD. While treatment modalities with an acute onset of action such as bronchodilators can be directly assessed by measurement of pulmonary function, treatments that target the underlying airway inflammation also have to use the physiologic outcome measure due to lacking alternatives although their mode of action is not related to airway obstruction.

Novel (anti-inflammatory) treatments that claim to modify the disease course need to be developed in due time. However, clinical trials that assess pulmonary function or the rate of exacerbations as the primary outcome are time- and cost-intensive. Therefore, there is a high medical need to develop novel biomarkers that are able to monitor treatment responses of disease-modifying investigational new drugs without the need for lengthy studies that capture the natural course of disease as the primary outcome. The pharmaceutical industry (Glaxo SmithKline) has initiated and conducted a large longitudinal observation of a COPD cohort (ECLIPSE) with almost 3,000 subjects followed up for 3 years in order to identify novel biomarkers for treatment responses. Hitherto, no valid biomarker has been identified that has the potential to improve the decision making.

While large consortia such as COPDGene have tried to identify and implement biomarkers for clinical decision making and monitoring of treatment responses of disease-modifying drugs, none has been successful so far. Therefore, there is a clear medical need to identify and establish novel biomarkers that are able to monitor the response to treatment and to improve diagnostic phenotyping. In particular, the identification of unstable patients with an unfortunate clinical course, such as frequent exacerbators and rapid decliners are of utmost importance. SUMMARY OF THE INVENTION

The present invention solves the above-mentioned problems, by providing for a method for the diagnosis of COPD, risk stratification, disease outcome, disease prognosis, or differential severity analysis of COPD in a subject that has or is suspected to have COPD, comprising the steps of, in a sample of said subject:

(a) determining the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof in a sample taken from said subject, wherein the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID

NO. 233 or fragments thereof is correlated with the diagnosis of COPD, a defined risk stratification, a defined disease outcome, a defined disease prognosis, or a differential severity of COPD in a subject,

wherein said diagnosis of COPD, defined risk stratification, defined disease outcome, defined disease prognosis, or a differential severity of COPD in a subject is given if the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is above or below a certain cut-off value.

The invention relates to biomarkers which are either significantly up- or down regulated.

The invention also relates to a primer or probe nucleic acid or equivalent thereto that hybridizes under stringent conditions to a transcript or fragment of a a nucleic acid transcript with a SEQ ID NO. 75 to SEQ ID NO. 233. It relates to uses and kits. This is the first ever COPD biomarker which allows for diagnosis, differential diagnosis and severity analysis and risk stratification.

Definitions In the context of the present invention, the terms "threshold", "threshold value", "cut-off" and "cut-off value" are used synonymously. The biomarkers herein are either up- or downregulated versus this cut-off value. The term "correlating," as used herein in reference to the use of diagnostic and prognostic markers, refers to comparing the presence or amount of the marker(s) in a patient to its presence or amount in persons known to suffer from, or known to be at risk of, a given condition; or in persons known to be free of a given condition. As discussed above, a marker level in a patient sample can be compared to a level known to be associated with a specific diagnosis. The sample's marker level is said to have been correlated with a diagnosis; that is, the skilled artisan can use the marker level to determine whether the patient suffers from a specific type diagnosis, and respond accordingly. Alternatively, the sample's marker level can be compared to a marker level known to be associated with a good outcome (e.g., the absence of disease, etc.). In preferred embodiments, a profile of marker levels is correlated to a global probability or a particular outcome.

As used herein, "nucleic acid(s)" or "nucleic acid molecule" generally refers to any ribonucleic acid or deoxyribonucleic acid, which may be unmodified or modified. "Nucleic acids" include, without limitation, single- and double-stranded nucleic acids. As used herein, the term "nucleic acid(s)" also includes nucleic acids as described above that contain one or more modified bases. Thus, a nucleic acid with one or several backbone modifications for stability or for other reasons is a "nucleic acid". The term "nucleic acids" as it is used herein encompasses such chemically, enzymatically or metabolically modified forms of nucleic acids, as well as the chemical forms of nucleic acids characteristic of viruses and cells, including for example, simple and complex cells. The transcripts referred to herein are nucleic acids.

A "prognosis" refers to assignment of a probability that a given course or outcome will occur. This is often determined by examining one or more "prognostic indicators". These are markers, the presence or amount of which in a patient (or a sample obtained from the patient) signal a probability that a given course or outcome will occur. For example, when one or more prognostic indicators reach a sufficiently high level in samples obtained from such patients, the level may signal that the patient is at an increased probability for eventually advancing into a higher GOLD stage of FEVi. There are a number of methods to determine how much COPD is affecting a given individual. The modified British Medical Research Council questionnaire (mMRC) or the COPD assessment test (CAT) are simple questionnaires that may be used to determine the severity of symptoms. Scores on CAT range from 0-40 with the higher the score, the more severe the disease. Spirometry may help to determine the severity of airflow limitation. This is typically based on the FEVi expressed as a percentage of the predicted "normal" for the person's age, gender, height and weight. Both the American and European guidelines recommended partly basing treatment recommendations on the FEVi. The GOLD guidelines suggest dividing people into four categories based on symptoms assessment and airflow limitation.

The terms "level" or "expression level" in the context of the present invention relate to the level at which a biomarker is present in a sample from a patient. The expression level of a biomarker is generally measured by comparing its expression level to the expression level of one or several housekeeping genes in a sample for normalisation. The sample from the patient is designated as COPD positive if the expression level of the biomarker exceeds the expression level of the same biomarker in an appropriate control (for example a healthy tissue) by a set threshold value. In some cases, it may be downregulated and the control may be higher.

The term "analysing a sample for the presence and/or level of nucleic acids" or "specifically estimate levels of nucleic acids", as used herein, relates to the means and methods useful for assessing and quantifying the levels of nucleic acids. One useful method is for instance quantitative reverse transcription PC . Likewise, the level of RNA can also be analysed for example by northern blot, next generation sequencing or after amplification by using spectrometric techniques that include measuring the absorbance at 260 and 280 nm. As used herein, the term "amplified", when applied to a nucleic acid sequence, refers to a process whereby one or more copies of a particular nucleic acid sequence is generated from a nucleic acid template sequence, preferably by the method of polymerase chain reaction. Other methods of amplification include, but are not limited to, ligase chain reaction (LCR), polynucleotide-specific based amplification (NSBA), or any other method known in the art.

The term "correlating", as used herein in reference to the use of diagnostic and prognostic marker(s), refers to comparing the presence or amount of the marker(s) in a sample from a patient to its presence or expression level in a sample from a person known to suffer from, or is at risk of suffering from, a given condition. A marker expression level in a patient sample can be compared to a level known to be associated with a specific diagnosis.

As used herein, the term "diagnosis" refers to the identification of the disease, in this case COPD, at any stage of its development, and also includes the determination of predisposition of a su bject to develop the disease.

The term "splice variant" refers to the product of an alternative splicing event. Alternative splicing events include exon skipping or inclusion, alternative 5' or 3' splice site usage, or intron retention.

As used herein, the term "fluorescent dye" refers to any chemical that absorbs light energy of a specific wavelength and re-emits light at a different wavelength. Fluorescent dyes suitable for labelling nucleic acids include for example FAM (5-or 6-carboxyfluorescein), VIC, N ED, Fluorescein, FITC, I D-700/800, CY3, CY5, CY3.5, CY5.5, H EX, TET, TAMRA, JOE, ROX, BODI PY TM R, Oregon Green, Rhodamine Green, Rhodamine Red, Texas Red, Yakima Yellow, Alexa Fluor, PET and the like.

As used herein, "isolated" when used in reference to a nucleic acid means that a naturally occurring sequence has been removed from its normal cellular (e.g. chromosomal) environment or is synthesised in a non-natural environment (e.g. artificially synthesised). Thus, an "isolated" sequence may be in a cell-free solution or placed in a different cellular environment.

As used herein, a "kit" is a packaged combination optionally including instructions for use of the combination and/or other reactions and components for such use. If the kit contains nucleic acids, the kit may also comprise synthetic or non-natural variants of said nucleic acids. A synthetic or non- natural nucleic acid is to be understood as a nucleic acid comprising any chemical, biochemical or biological modification, such that the nucleic acid does not appear in nature in this form. Such modifications include, but are not limited to, labelling with a fluorescent dye or a quencher moiety, a biotin tag, as well as modification(s) in the backbone of a nucleic acid, or any other modification that distinguishes the nucleic acid from its natural counterpart. The same applies also to other natural compounds such as proteins, lipids and the like.

The term "patient" as used herein refers to a living human or non-human organism that is receiving medical care or that should receive medical care due to a disease, or is suspected of having a disease. This includes persons with no defined illness who are being investigated for signs of pathology. Thus, the methods and assays described herein are applicable to both, human and veterinary disease.

The term "primer" as used herein, refers to a nucleic acid, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand, is induced, i.e., in the presence of nucleotides and an inducing agent such as a DNA polymerase and at a suitable temperature and pH. The primer may be either single-stranded or double-stranded and must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer will depend upon many factors, including temperature, source of primer and the method used. Preferably, primers have a length of from about 15-100 bases, more preferably about 20-50, most preferably about 20-40 bases. The factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art. Optionally, the primer can be a synthetic element, in the sense that it comprises a chemical, biochemical or biological modification. Such modifications include, but are not limited to, labelling with a fluorescent dye or a quencher moiety, or a modification in the backbone of a nucleic acid, or any other modification that distinguishes the primer from its natural nucleic acid counterpart. The term "probe" refers to any element that can be used to specifically detect a biological entity, such as a nucleic acid, a protein or a lipid. Besides the portion of the probe that allows it to specifically bind to the biological entity, the probe also comprises at least one modification that allows its detection in an assay. Such modifications include, but are not limited to labels such as fluorescent dyes, a specifically introduced radioactive element, or a biotin tag. The probe can also comprise a modification in its structure, such as a locked nucleic acid.

The term "fragment" refers to, e.g. a splice variant or another shorter form of the m NA transcript. A gene may result in different mRNA forms. These are encompassed by the term fragment. DETAILED DESCRIPTION OF THE INVENTION

The method according to the invention also involves comparing the level of marker for the individual/patient/subject to diagnosed with a predetermined value. The predetermined value can take a variety of forms. It can be single cut-off value. This can be for instance a median or mean or the 75^th, 90^th, 95^th or 99^th percentile of a reference population. This can be for instance also an "optimal" cut-off value. The optimal cut-off value for a given marker is the value where the product of diagnostic sensitivity and specificity is maximal for this marker.

The predetermined value can be established based upon comparative groups, such as where the risk in one defined group is double the risk in another defined group. It can be a range, for example, where the tested population is divided equally (or unequally) into groups, such as a low-risk group, a medium-risk group and a high-risk group, or into quartiles, the lowest quartile being individuals with the lowest risk and the highest quartile being individuals with the highest risk. The predetermined value can vary among particular reference populations selected, depending on their habits, ethnicity, genetics, sex etc. Accordingly, the predetermined values selected may take into account the category in which individual falls. Appropriate ranges and categories can be selected with no more than routine experimentation by those of ordinary skill in the art. In certain embodiments, particular thresholds for one or more markers in a panel are not relied upon to determine if a profile of marker levels obtained from a subject are indicative of a particular diagnosis/prognosis. Rather, the present invention may utilize an evaluation of a marker panel "profile" as a unitary whole. A particular "fingerprint" pattern of changes in such a panel of markers may, in effect, act as a specific diagnostic or prognostic indicator. As discussed herein, that pattern of changes may be obtained from a single sample, or from temporal changes in one or more members of the panel (or a panel response value). A panel herein refers to a set of markers.

A panel response value can be derived by various methods. One example is Cox proportional hazards analysis. Another example is optimizing ROC curves: This can be achieved by plotting ROC curves for the sensitivity of a particular panel of markers versus l-(specificity) for the panel at various cut-offs.

In these methods, a profile of marker measurements from a subject is considered together to provide a global probability (expressed either as a numeric score or as a percentage risk) of a diagnosis or prognosis. In such embodiments, an increase in a certain subset of markers may be sufficient to indicate a particular diagnosis/prognosis in one patient, while an increase in a different subset of markers may be sufficient to indicate the same or a different diagnosis/prognosis in another patient. Weighting factors may also be applied to one or more markers in a panel, for example, when a marker is of particularly high utility in identifying a particular diagnosis/prognosis, it may be weighted so that at a given level it alone is sufficient to signal a positive result. Likewise, a weighting factor may provide that no given level of a particular marker is sufficient to signal a positive result, but only signals a result when another marker also contributes to the analysis. The invention also relates to a method for the diagnosis of COPD, in a subject that has or is suspected to have COPD, comprising the steps of, in a sample of said subject:

(a) determining the level of one or more transcripts according to SEQ I D NO. 75 to SEQ I D NO. 158 or fragments thereof in a sample taken from said subject, wherein the level of one or more transcripts according to SEQ I D NO. 75 to SEQ I D NO. 158 or fragments thereof is correlated with the diagnosis of COPD in a subject,

wherein said diagnosis of COPD in a subject is given if the level of one or more transcripts according to SEQ I D NO. 75 to SEQ ID NO. 233 or fragments thereof is above or below a certain cut-off value.

Transcripts with SEQ I D NO. 75 to SEQ I D NO. 158 have been shown to be particularly useful in diagnosis. Up- and down regulation is as follows. For all biomarkers of probes SEQ I D NO. 1 to SEQ I D NO. 35 the respective expression is significant if these are up-regulated.

For all biomarkers of probes SEQ ID NO. 52 to SEQ ID NO. 74 the respective expression is significant if these are up-regulated.

For all biomarkers of probes SEQ ID NO. 36 to SEQ ID NO. 51 the respective expression is significant if these are up-regulated (+1) or down-regulated (-1) as follows for inflammation per se

For all biomarkers of probes SEQ ID NO. 36 to SEQ ID NO. 51 the respective expression is significant if these are up-regulated (+1) or down-regulated (-1) as follows for eosinophilic inflammation

36 -1

37 1

38 1

39 -1

40 -1

41 -1

42 -1

43 -1

44 1

45 -1

46 1

47 -1

48 -1

49 -1

50 1

51 -1

For all biomarkers of probes SEQ ID NO. 36 to SEQ ID NO. 51 the respective expression is significant if these are up-regulated (+1) or down-regulated (-1) as follows for neutrophilic inflammation

47 -1

48 1

49 -1

50 1

51 -1

Diagnostic sensitivity is the relative fraction of patients, carrying the disease or the risk for developing the disease (depending on the diagnostic or prognostic question to be answered in any particular case), which are correctly recognized as such by a marker ("true positives"), and the diagnostic specificity is the relative fraction of patients, not carrying the disease or the risk for developing the disease (depending on the diagnostic or prognostic question to be answered in any particular case), which are recognized as such by a marker ("true negatives"). This can by a cut-off value optimized for a maximal negative predictive value or maximal positive predictive value, depending on clinical or economical needs. Thereby optimizing specificity and sensitivity.

Thus, one might adopt the cut-off value depending on whether it is considered more appropriate to identify most of the subjects at risk at the expense of also identifying "false positives", or whether it is considered more appropriate to identify mainly the subjects at high risk at the expense of missing several subjects at moderate risk.

The invention preferably concerns a method wherein the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is preferably correlated with the said diagnosis of COPD, defined risk stratification, defined disease outcome, defined disease prognosis or a differential severity of COPD in a subject by a method which is selected from the following alternatives: correlation with respect to the median of the level in an ensemble of predetermined samples,

correlation with respect to quantiles in an ensemble of pre-determined samples, and

correlation with a mathematical model, such as for example Cox Regression.

In certain embodiments, markers and/or marker panels are selected to exhibit at least about 70% sensitivity, more preferably at least about 80% sensitivity, even more preferably at least about 85% sensitivity, still more preferably at least about 90% sensitivity, and most preferably at least about 95% sensitivity, combined with at least about 70% specificity, more preferably at least about 80% specificity, even more preferably at least about 85% specificity, still more preferably at least about 90% specificity, and most preferably at least about 95% specificity. In particularly preferred embodiments, both the sensitivity and specificity are at least about 75%, more preferably at least about 80%, even more preferably at least about 85%, still more preferably at least about 90%, and most preferably at least about 95%. The term "about" in this context refers to +/- 5% of a given measurement. In other embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, or hazard ratio is used as a measure of a test's ability to predict risk or diagnose a disease. In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "diseased" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the diseased group; and a value less than 1 indicates that a positive result is more likely in the control group. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both the "diseased" and "control" groups; a value greater than 1 indicates that a negative result is more likely in the test group; and a value less than 1 indicates that a negative result is more likely in the control group. In certain preferred embodiments, markers and/or marker panels are preferably selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, more preferably at least about 2 or more or about 0.5 or less, still more preferably at least about 5 or more or about 0.2 or less, even more preferably at least about 10 or more or about 0.1 or less, and most prefera bly at least about 20 or more or about 0.05 or less. The term "about" in this context refers to +/- 5% of a given measurement.

In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "diseased" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the diseased group; and a value less than 1 indicates that a positive result is more likely in the control group. In certain preferred embodiments, markers and/or marker panels are preferably selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, more preferably at least about 3 or more or about 0.33 or less, still more preferably at least about 4 or more or about 0.25 or less, even more preferably at least about 5 or more or about 0.2 or less, and most preferably at least about 10 or more or about 0.1 or less. The term "about" in this context refers to +/- 5% of a given measurement. In the case of a hazard ratio, a value of 1 indicates that the relative risk of an endpoint (e.g., death) is equal in both the "diseased" and "control" groups; a value greater than 1 indicates that the risk is greater in the diseased group; and a value less than 1 indicates that the risk is greater in the control group. In certain preferred embodiments, markers and/or marker panels are preferably selected to exhibit a hazard ratio of at least about 1.1 or more or about 0.91 or less, more preferably at least about 1.25 or more or a bout 0.8 or less, still more preferably at least about 1.5 or more or about 0.67 or less, even more preferably at least about 2 or more or about 0.5 or less, and most prefera bly at least about 2.5 or more or a bout 0.4 or less. The term "about" in this context refers to +/5% of a given measurement.

The skilled artisan will understand that associating a diagnostic or prognostic indicator, with a diagnosis or with a prognostic risk of a future clinical outcome is a statistical analysis. For example, a marker level of greater than X (or smaller than) may signal that a patient is more likely to suffer from an adverse outcome than patients with a level less than or equal to X, as determined by a level of statistical significance. Additionally, a change in marker concentration from baseline levels may be reflective of patient prognosis, and the degree of change in marker level may be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations, and determining a confidence interval and/or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983. Preferred confidence intervals of the invention are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while preferred p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001.

Ideally, in a method of the invention the cut-off value of the level of one or more transcripts according to SEQ I D NO. 75 to SEQ ID NO. 233 or fragments thereof is about 1.5 fold (± 20%), 2 fold (± 20%), 3 fold (± 20%), 4 fold (± 20%) and most preferably 5 fold (± 20%) or more, higher than (or lower than) the amount of the control sample, and may deviate depending on the patient analysed by about 5%, 8%, 10%, or 20%.

The cut-off values are in a very preferred embodiment:

COPD diagnosis/inflammation :

1.5 (control to GOLD I)

2.5 (GOLD to GOLD II)

3.5 (GOLD II to GOLD I II) 4.5 (GOLD III to GOLD IV).

The invention also relates to a method according to any of the preceding claims comprising the steps of

1) determining the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof; and

2) correlating the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof with a per quantile risk of COPD stability or declining condition of COPD.

The invention also relates to a method for determining the differential severity of COPD in a subject according any of the preceding claims, wherein the subject is under a COPD condition selected from the group of

comprising the steps of:

(a) determining the level of one or more transcripts according to SEQ ID NO.

204 to SEQ ID NO. 233 or fragments thereof in a sample taken from said subject, and

(b) correlating the level of one or more transcripts according to SEQ ID NO.

204 to SEQ ID NO. 233 with said severity according to GOLD 1, GOLD 2, GOLD 3, or GOLD 4 and/or >80 FEVi %, 50-79 FEVi %, 30-49 FEVi %, <30 FEVi % or chronic respiratory failure.

In yet other embodiments, multiple determinations of diagnostic or prognostic markers can be made, and a temporal change in the marker can be used to determine a diagnosis or prognosis. For example, a marker concentration in a subject sample may be determined at an initial time, and again at a second time from a second subject sample. In such embodiments, an increase in the marker from the initial time to the second time may be indicative of a particular diagnosis, or a particular prognosis. Likewise, a decrease in the marker from the initial time to the second time may be indicative of a particular diagnosis, or a particular prognosis.

The invention is thus also suited for a method of medical decision making for individual patient therapy related to the severity of the COPD disease by monitoring therapy response to a certain drug in a subject with sepsis or a sepsis like disease, comprising the steps of (a) - (c):

(a) providing for at least two samples from said subject at various time points selected from the group of i. before initiation of therapy,

ii. after initiation of therapy, and/or

iii. at one or more further time point;

(b) determining the level of level of one or more transcripts according to SEQ ID NO. 1 to SEQ ID NO. 233 or fragments thereof in said sample,

(c) associating the level of level of one or more transcripts according to SEQ ID NO. 1 to SEQ ID NO. 233 or fragments thereof in said samples with a positive or a negative response to said certain drug; wherein said positive response is given if the level of one or more transcripts according to SEQ ID NO. 1 to SEQ ID NO. 233 or fragments thereof decreases (or increases) during drug treatment, and preferably further adapting therapy to the response to said drug.

Whether it is an increase or decrease is dependant on the specific marker.

COPD may need to be differentiated from other causes of shortness of breath such as congestive heart failure, pulmonary embolism, pneumonia or pneumothorax. Many people with COPD mistakenly think they have asthma. The distinction between asthma and COPD is made on the basis of the symptoms, smoking history, and whether airflow limitation is reversible with bronchodilators at spirometry. Tuberculosis may also present with a chronic cough and should be considered in locations where it is common. Less common conditions that may present similarly include bronchopulmonary dysplasia and obliterative bronchiolitis. Chronic bronchitis may occur with normal airflow and in this situation it is not classified as COPD. The present invention is able to differentially diagnose COPD and the other causes, congestive heart failure, pulmonary embolism, pneumonia, pneumothorax, asthma, tuberculosis, bronchopulmonary dysplasia and chronic bronchitis.

The invention relates very preferably to the following uses and methods:

Probes according to SEQ Diagnosis of severity of COPD in patients:

ID NO. 52 to SEQ ID NO. using 1 of these probes, primers

74 or equivalents using 1-5 of these probes, primers

thereto using 1 to 10 of these probes, primers

using all the probes, primers

The invention relates to a method for differentially diagnosing between COPD and another disease in a subject, wherein the disease is either COPD or alternatively another disease selected from the group comprising (i) congestive heart failure, (ii) pulmonary embolism, (iii) pneumonia, (iii) pneumothorax, (iv) asthma, tuberculosis, (v) bronchopulmonary dysplasia and (vi) chronic bronchitis, comprising the steps of:

(a) determining the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO.

233 or fragments thereof in a sample taken from said subject, and

(b) correlating the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO.

233 or fragments thereof to either (i) COPD or (ii) another disease, and wherein said correlation of the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof to (i) COPD is given if the level is above (or below) a certain cut-off value and no correlation to COPD is given if the value is below said cut-off level.

The sample is selected from a group comprising: a plasma sample, a serum sample, a whole blood sample, a blood sample or fractions thereof, a lymphatic fluid sample, a urine sample, a lung tissue, sputum, saliva, biopsy material, lymph nodes, urine, ejaculate, blood, blood serum, blood plasma, circulating tumour cells in blood or lymph, exosomes, micro vesicles, blood cells, leukocytes, monocytes, lymphocytes and neutrophils. The term "sample" as used herein refers also to a sample of bodily fluid obtained for the purpose of diagnosis, prognosis, or evaluation of a subject of interest, such as a patient. Preferred test samples include blood, serum, plasma, cerebrospinal fluid, urine, saliva, sputum, and pleural effusions. In addition, one of skill in the art would realize that some test samples would be more readily analyzed following a fractionation or purification procedure, for example, separation of whole blood into serum or plasma components.

Thus, in a preferred embodiment of the method according to the invention said sample is an isolated fraction of monocytes. The sample is also preferably sputum. The invention relates to a method of risk stratification in COPD encompassing determining whether the patient has an inflammation of the lung comprising inflammatory granulocytes, such as eosinophils and/or neutrophils comprising: (a) determining the level of one or more transcripts according to SEQ ID NO. 159 to SEQ ID NO.

202 or fragments thereof in a sample taken from said subject, and

(b) correlating the level of one or more transcripts according to SEQ ID NO. 159 to SEQ ID NO.

202 or fragments thereof to either (i) COPD with inflammation and higher risk or (ii) COPD without inflammation and a lower risk, and wherein said correlation of the level of one or more transcripts according to SEQ ID NO. 159 to SEQ ID NO. 202 or fragments thereof to (i) COPD with inflammation is given if the level is above a certain cut-off value and no correlation to COPD without inflammation is given if the value is below said cut-off level.

As outlined above the level may also be below the cut-off. This depends on the marker.

The method/therapy may be further adapted in so far as the inflammation is concerned. Then anti- inflammation drugs are given.

The invention relates to a primer or probe nucleic acid or equivalent thereto that hybridizes under stringent conditions to a transcript or fragment of a nucleic acid transcript with a SEQ ID NO. 75 to SEQ ID NO. 233. Ideally, the primer or probe is selected from the group of SEQ ID NO. 1 to 74. The primer or probe may be any other probe as long as it specifically binds SEQ ID NO. 75 to SEQ ID NO. 233 under stringent conditions. Ideally, the primer or probe is labelled.

The invention also relates to uses of the primers and probes and transcripts in the methods of the invention.

The following probes have been found to be very suited for COPD diagnosis.

Seq ID 1 A_21_P0001647

ACTGTATAACCTAAGAGTCGGGTGCAGGCTTGATAAAGAATTGGAAGCCCTCTGCAAAAC Seq ID 2 A_21_P0002437

GGCAGAGCATCTGTAGTGGAGAGAATGGGATTTTAGAGGAACGCTGAAAATAAAACAAGA Seq ID 3 A_21_P0003691

TTATGTTCTCTGAAGACACTACAGTTTAAATCCACATTGGGGAACTCCCCCACTAGCCAG

Seq ID 4 A_21_P0007693

GAAGGCAAAGACTTTCATTCAATCCTGCTATCCTGTCAACTTCGATAAACCTAGTCCCGG

Seq ID 5 A_21_P0010460

ACAGCCAACTTCAGGGGAGCTGTGTTGGATCCCTCCAAACTCCGGGAATGATGTGACCTC

Seq ID 6 A_21_P0010592

GAAAAATTCAGACCCAGCACAGTGTTTATGTTGGTCAAAAATAGAAAACTATGTCTGGCG

Seq ID 7 A_21_P0014552

TTCTCAACCTTCCAAATGTTAGGAGTTTAAGCTCTTGCCTGGACTCCTTAAGTTGGCCCA Seq ID 8 A_23_P106002

TGATGTGGGGTGAAAAGTTACTACCTGTCAAGGTTTGTGTTACCCTCCTGTAAATGGTGT

Seq ID 9 A_23_P207564

CAGGAAGTCTTCAGGGAAGGTCACCTGAGCCCGGATGCTTCTCCATGAGACACATCTCCT

Seq ID 10 A_23_P368909

TACTTGCGCACGTCTGGTAGCTGCCCTGGACATTCATGGACTTTCGTTTTCACTCAGATT

Seq ID 11 A_23_P406071

ATAAAAAACCGAAATAGCTTCACATCAGAACTCAGACTAACCTTGTGGTTTCAACGCGCA

Seq ID 12 A_23_P431179

TTTGGCGGTTAAGGTTGCTGATTTCTCCACAGCTTGCATTTCTGAACCAAAGGCCCTTTT Seq ID 13 A_23_P74609

CCAACACTGTGTGAATTATCTAAATGCGTCTACCATTTTGCACTAGGGAGGAAGGATAAA

Seq ID 14 A_24_P228130

TTCCACAGAATTTCATAGCTGACTACTTTGAGACGAGCAGCCAGTGCTCCAAGCCCAGTG Seq ID 15 A_24_P37409

AGTATCCCTGTGGAGGACAACCAGATGGTGGAGATCAGTGCCTGGTTCCAGGAGGCCATA

Seq ID 16 A_32_P60459

GTGTAGAGTAGATTGTCTGGTGCTCTCAGTTGTTTTTATTTACATTTGTCACGTTGTTGT

Seq ID 17 A_32_P886589

CACCCTTCGGGGAATTCCCGTTCAGCTCTACAGGAGGCGAAAACGGAACAAACGAAAACC Seq ID 18 A_33_P3243230

AAGTTCTTTGTCACTCCCAGTAGTGTCCTATTTTAGATGATAATTTCTTTGATCTCCCTA

Seq ID 19 A_33_P3274622

AATTCAGACCCAGCACAGTGTTTATGTTGGTCAAAAATAGAAAACTATGGCGCGGCCGAG

Seq ID 20 A_33_P3284039

GTGCCTTTATAACCACCGCGTTGGAAGGAAGAGCGCGGAGAAAGGTGCCCACTTTCTAAA

Seq ID 21 A_33_P3289745

AAATGTGGAAATGCATGGCCTGTTAAAGCTGCCTTAAGAAAATATGTTGCCTGGGGCTGG

Seq ID 22 A_33_P3296181

AAGAGTAGTCAGTCCCTTCTTGGCTCTGCTGACACTCGAGCCCACATTCCATCACCTGCT Seq ID 23 A_33_P3312985

TAAAACATCAATTTAACATACAGATAATGCTGGGACTGCAGCTTCGCCAGGTGGCCTTCC

Seq ID 24 A_33_P3316273

TGCTTTTGTTCAGGGCTGTGATCGGCCTGGGGAAATAATAAAGATGCTCTTTTAAAAGGT

Seq ID 25 A_33_P3323298

CTTCCAATTTGGAATCTTCTCTTTGACAATTCCTAGATAAAAAGATGGCCTTTGCTTATG

Seq ID 26 A_33_P3339701

ACAGCAAGCGTCAAAAGACTGCACAGCAGGAAGGCGAAACCGAACAGAACTGCTGCTGCG

Seq ID 27 A_33_P3354589

CATGGACCATGGTCAGGCAGAGGAAGATGCCTACCACAGGCAAGGGATAAAGCCAGATGA

Seq ID 28 A_33_P3354604

ATCCAGTGAGTGGAAGTTACAGGGAGTCTGCTTCCAGTGCTGCTCCGGGAAGGATCCCAT Seq ID 29 A_33_P3354607

AAGTCTGTGCTGATCCCAGTGAATCCTGGGTCCAGGAGTACGTGTATGACCTGGAACTGA Seq ID 30 A_33_P3354881

CGCTGGCAGCGTCTGAGAGCTGTCCTGGGCCGGAGGTGGCGCGAACCCTGTGCGGCCTAG Seq ID 31 A_33_P3365352

AGAAAAAGCAGCGGTGACAGCCTTTGGTCCCCATCTCCATTGTTCCTGCCAGCTCTGGAC Seq ID 32 A_33_P3368034

TAGTAGAAGAAACAAACCCAAAATGAAAACACCCGGATTTTAAGCGCGGATCCTGCGAGG

Seq ID 33 A_33_P3381671

AGAACTACAGGAAAGGAGTAGTATCTCTGAATTATCGTGTGGAAGGTCGCTTGTCTAGCC

Seq ID 34 A_33_P3400023

GGAGAAACTAGCTCCGGAAGCACTTTCTCATGACTCTGCTTAATAAAGGAATAAATAGTA Seq ID 35 A_33_P3424347

CTTAACCAGTGACATCTGCTGTAACTGTTTTCTTTGAGATTAATAAATGGACCTTTTTCC

The following probes have been found to be very suited for risk stratification and inflammation detection.

Seq ID 36 A_21_P0006020

CAGATGACCTACAGGATTTCTGTTCCTTTCCAGCTTTTTATTTTACCATTGATGCTTCCT

Seq ID 37 A_21_P0011966

TAAAAGCTACTCCAATGGCTGTGGGAGACTTAATTCCAATTCCAGGTGATACAGCCGTCA

Seq ID 38 A_21_P0013816

AACCTTAGAACCACAGTAAAATGTGTGACCTTAGCAATATATCACCACATAAAGAACAGA Seq ID 39 A_23_P141974

AGGCCAAAGAAGAGAACGTGGGCTTACATCAGACACTGGATCAGACACTAAACGAACTTA

Seq ID 40 A_23_P41292

CTTGTCTGTGTACAGTTTTTAGAACATTACAAAGGATCTGTTTGCTTAGCTGTCAACAAA Seq ID 41 A_24_P411561

AGTGATCCTTAAAAGATTAAGAGATGACTGGACTAGGTCTACCTTGATCTTGAAGATTCC

Seq ID 42 A_24_P82880

GTGACCTGGAAGAAGAACTCAAGAATGTTACTAACAATCTGAAATCTCTGGAGGCTGCAT

Seq ID 43 A_32_P46238

GGGCACTGGTCCATGACCTGTTGTCTTTCTGTATCTACTTTCTGCAGCCCCTCACTGAGG Seq ID 44 A_32_P73045

AAGATGCTATTGGGATAAGTTTATTAAAGAAAAGTGGTATTGAGGTGAGGCTAATCTCAG

Seq ID 45 A_33_P3263379

AGCAGGGACGGGGCCCTCTGAGACCCATCTCACAAAGATGAGTGGTGAAAATCTGATCAC

Seq ID 46 A_33_P3322125

AATATAACTTACAAGCCAGAGTGAGCCCATTAATGGATTTGGTCAGGCTCCCTCTGGAGA

Seq ID 47 A_33_P3343828

AAACATTGTACACAACAGCCTGGTGGTCTCTAAAGCCAACAGTGTCCTGTACCCTGAAAT

Seq ID 48 A_33_P3373364

ATGACTGGCATCTGGAGATACCTAACTAATGCATATAGTAGGGACGAGTTCATCAATACC Seq ID 49 A_33_P3380751

CGAAGAAGCGTGCGTGCGTTTGCAAGTAAGAGAACCAAAGGTGTGTGTGCATGGGGGGCT

Seq ID 50 A_33_P3399453

CATGGAATACTGTTGAACCTATAGCATTGTCTGATTCTTTTGTGTTCTCTGCTTTGTAAT

Seq ID 51 A_33_P3399474

AAGAAGTAACTGAAACTAACCCAAGGGTTACAACCGAAAAGCCCCTTCCAGCTTCAGAAG

The following probes have been found to be very suited for analysis of severity.

Seq ID 52 A_19_P00320536

TGTAAGACTCCTGGACTCCTTAGGTTTTATCAATCCTGGTGCCCTTCCATTGTATCACAT

Seq ID 53 A_19_P00321388

TCTTAGCTGCTATGTGCAAGACGTGTGTCCCAGGGAAAGCCCCTCTCTCTCTGCAGAGGT Seq ID 54 A_21_P0001743

ACTTAAAATACTCAAAATCCTAGAAGGTGGACTAGTGGGAAGTCTCTCTCCCTGAAGCAG Seq ID 55 A_21_P0002840

TTACTGCCTGCAATTGTTGGTTTAAACATCTGCCTCCCCAGAAGACTTGAGGGCAGAAAA

Seq ID 56 A_21_P0003497

GGTCACCTTTGAACAGTGCCTCCCTCTCTGAAATAAAATACTGGTTCTGTCATTAATTAT

Seq ID 57 A_21_P0005108

TCCACCACCATCTTACAAGTTTGTGCTACGGGTGGAGGGAAAATGAATCTAATGTAGGAG

Seq ID 58 A_21_P0012691

AGTTGGTGGCCCGATCCATAAGAGAAAGGAAAATGAGACAACATTATAAGTCATGTAAAG

Seq ID 59 A_23_P108265

CAATAACATCGTGATGTATTTTGTGACCATTGTCCTGGGTGTTTTTCCTCTCTGTGGAAT Seq ID 60 A_23_P1998

ATGGCCGAACGGCGTAATGCCCGCTGCTTGGTAAATGGACTCTCCCTGGACCACTCTAAA

Seq ID 61 A_23_P368909

TACTTGCGCACGTCTGGTAGCTGCCCTGGACATTCATGGACTTTCGTTTTCACTCAGATT

Seq ID 62 A_23_P73637

CTTCACCAGCAAAGGTCACAGAACGGGTTCCAACTCAGATGCCTTTCAACTGGGGGGCCT

Seq ID 63 A_23_P8142

ATCTCCCGGGCTGGCCACCTCCTTGACCAGCATATCTGTTTTCTGATTGCGCTCTTCACA

Seq ID 64 A_24_P271323

CCCAAGGACTCTGGCCTCTCGAGTTCTCCTATCTTCTCCATTCTAGATGCTTCCCTTGTA Seq ID 65 A_32_P192594

CCCTGGCGGTTCTTAGTGGGATTACAATTGAGGATGTTAGTTTGGATGAAAGTTGGTGAA

Seq ID 66 A_33_P3321689

CAGGGCTTCCAGCTCCCTGCTTCCTGTCTAAGTCCAGGTACTTACCCCACCTATAGGTGA Seq ID 67 A_33_P3322945

TCAAAAACGCCTGGGGTGGGGACTTTCTCATCGTCTTGCCTCCCCAGATGCAACTGGAAC

Seq ID 68 A_33_P3338724

CAAAC CA AGCACA CC CCAGT ACA GGCAGAAGAGGGAGGGAGGGAGGGCCAAAAA

Seq ID 69 A_33_P3352213

CCACCACAGAGGAACAGAGAAGTGTTTGCATTGGTGGATTTTTAAATACTTGTTTATTTT Seq ID 70 A_33_P3356696

AACGCATTGTGGGGGAAAGAATGGCAGTTCTCCGCTGTGTGGAGTCTCTCACCAGGCCTA

Seq ID 71 A_33_P3378291

GGCCTTCTTTTCCCTCCTGCAACAGAGGCTGCTATGTCCCATAGACTGGAGAGGGGGCTG

Seq ID 72 A_33_P3402838

TTCCTTCCACGTGCGTGAGAAGGTGCGCGAGGAGACCAACACGCGATCCTTCGACCGCAT

Seq ID 73 A_33_P3407925

GGTGTCTTACAAGTGAGCTGACACCATTTTTTATTCTGTGTATTTAGAATGAAGTCTTGA

Seq ID 74 A_33_P3413227

TTTGCATATTCTGTTGCTGTGATCTGAGACGGCCCCTCTCAGAAGCGGGTGCCACAACCC Hence, it relates to a nucleic acid with a SEQ I D NO. 75 to SEQ ID NO. 233 for the diagnosis of COPD, risk stratification, disease outcome, disease prognosis, or differential severity analysis of COPD in a subject that has or is suspected to have COPD.

The SEQ I D NO. 1 to 74 are probes and are as such anti-sense to their respective transcripts.

When a claim herein refers to, e.g. a method for the diagnosis of COPD, risk stratification, disease outcome, disease prognosis, or differential severity analysis of COPD in a subject that has or is suspected to have COPD, comprising the steps of, in a sample of said subject: (a) determining the level of one or more transcripts according to SEQ ID NO. 75 to SEQ I D NO. 233 or fragments thereof in a sample taken from said subject, (b) wherein the level of one or more transcripts according to SEQ I D NO. 75 to SEQ I D NO. 233 or fragments thereof is correlated with the diagnosis of COPD, a defined risk stratification, a defined disease outcome, a defined disease prognosis, or a differential severity of COPD in a subject wherein, (c) said diagnosis of COPD, defined risk stratification, defined disease outcome, defined disease prognosis, or a differential severity of COPD in a subject is given if the level of one or more transcripts according to SEQ I D NO. 75 to SEQ ID NO. 233 or fragments thereof is above or below a certain cut-off value, then this applies also to the reverse complement of the SEQ ID given above.

All SEQ I D NOs. include also the reverse complements thereof, as in some cases the sequences given herein are the m NA sequence and in other cases the cDNA sequences.

FIGURE CAPTIONS

Figures 1 to 74 show the plotting of ROC curves for the sensitivity of a particular panel of markers versus l-(specificity) for the panel at various cut-offs. Those markers are preferred that show very good values. They will not all be designated here as this is evident from the ROC curves. Diagnosis of COPD: a linear, additive, multivariate model, which is used with covariates: type: COPD, healthy; smoker: yes, no; gender: male, female.

Diagnosis of inflammation and type (neutrophilic or eosinophilic): a linear, additive, multivariate model was applied using covariates: type: inflammation, no inflammation, neutrophilic, eosinophilic; smoker: yes, no; gender: male, female. The markers from the monocytes can predict the inflammation status of the lung and the type of inflammation in the lung.

Diagnosis of COPD: a linear, additive, multivariate model, with applied covariates: type: control GOLD l-IV; smoker: yes, no; gender: male, female.

EXAMPLES

Description of the Cohort

For a cohort of 90 COPD patients of disease severity grades GOLD I -IV and 60 control su bjects (30 smokers and 30 non-smokers), transcriptomes of sputum and blood samples were analysed by DNA microarrays. From blood, analyses were done in RNAs derived from whole-blood samples as well as from isolated lymphocytes, granulocytes, and monocytes. Patients and controls consisted of 150 subjects of both genders aged 40 to 75 years with a smoking history of >10 pack years (smokers only) and evidence (COPD patients) or absence (healthy controls) of airway obstruction (FEV1/FVC < 70%). Patients with COPD were classified into severity stages according to the Global Initiative for Obstructive Lung Disease [GOLD guideline - Update 2015] according to their pulmonary function data leading to three COPD groups with 30 patients each. While comorbid diseases were not excluded when not found to affect the study outcome, respiratory infections in the previous four weeks were excluded.

Sample collection

For analysis of whole-blood, blood samples were collected in PAXgene RNA tubes (Preanalytix) and cryo-preserved at -80°C. For separation of lymphocytes, granulocytes and monocytes from whole blood the pluriBead cell separation system (pluriSelect) was used. Separated blood cells were lysed in 1ml Qiazol (Qiagen) and stored at -80°C. Beside whole blood and separated blood cells, sputum samples were collected and stored in 1ml Qiazol at -80°C.

RNA isolation

Total RNA (including miRNA) was isolated using the PAXgene miRNA Kit (for whole blood samples) and the miRNeasy Mini Kit (for blood cells and sputum samples) on the QIAcube instrument (all from Qiagen), respectively. Subsequent manual DNase I (Turbo DNA free kit, Life Technologies) digestion was performed to obtain DNA-free RNA samples. RNA concentration was further determined using a Nanodrop 1000 (Peqla b). RNA integrity was verified on an Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA), and only RNA samples with an RNA-lntegrity-Number (RIN) of at least 6 were further processed. Biomarker screening by microarrays

The microarray screening was performed using microarrays with 8 x 60 000 probes (Agilent SurePrint G3 Human Gene Expression v3 8x60K Microarray Kit). Whole blood, blood cell and sputum samples, each 40, of the prospective COPD cohort were analysed. Using the Quick Amp Labeling Kit (Agilent) cRNA was synthesized from 200 ng total RNA, and 600 ng cRNA was hybridized on the arrays (Agilent Gene Expression Hybridization Kit).

Data analysis and bioinformatics

Overall, 389 samples were analysed for four different sample types (40 Lymphocytes, 137 Monocytes, 138 whole blood, 74 sputum samples). After quality assessment based on spike-in controls, outliers, and distributions of expression, 9 microarrays were excluded from further analysis. All microarrays were pre-processed, background corrected, and normalized through quantile adjustment. Differential expression for three paradigms was tested: COPD vs Healthy; progression from healthy to COPD GOLD I up to GOLD IV; and finally differences between COPD patients with and without inflammation of neutrophils or eosinophils in sputum probes.

Patients where declared positive for eosinophilic inflammation when the percentage of eosinophils cells in non-squamous sputum cells was at least 2%. Furthermore, patients were declared positive for neutrophilic inflammation when percentage of neutrophilic cells of non-squamous sputum cells was at least 61%. When both conditions were applicable, the patient was given the status of mixed inflammation type.

Detection of differential expressions for all contrasts were conducted using linear effect models with probe intensity as main effect and sex, smoking status, and batch number as co-variates. Resulting p values were corrected for multiple testing and candidate probes were selected based on statistical significance, AUC, and log fold changes. Finally, contrasts where visualised based on predictions by linear effect models using the previously described model terms.

All bioinformatics analyses where conducted using R statistical software and additional software packages, notably limma, fdrtool, and pROC, beside others.

The data demonstrate that sputum RNA patterns differed significantly between COPD and controls while whole-blood RNA patterns did not. In contrast to whole blood, RNAs from isolated monocytes carried RNA biomarkers that discriminated COPD from controls almost as well as RNAs from sputum.

Lymphocyte RNA patterns were also discriminative, yet yielded lower sensitivity and specificity values than monocyte and sputum RNAs. Furthermore, patterns from both, sputum and monocytes correlated with the progress of COPD, as defined by the clinical GOLD standard.

Furthermore, we could show that the combination of two, three, or more of these genes/transcripts improve diagnostic and prognostic predictive power considerably. In addition, the inflammatory status of the lung as judged by the presence of infiltrating granulocytes in sputum could be predicted by RNA biomarker patterns in blood monocytes with high specificity and sensitivity.

Claims

A method for the diagnosis of COPD, risk stratification, disease outcome, disease prognosis or differential severity analysis of COPD in a subject that has or is suspected to have COPD, comprising the steps of, in a sample of said subject: determining the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof in a sample taken from said subject, wherein the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is correlated with the diagnosis of COPD, a defined risk stratification, a defined disease outcome, a defined disease prognosis, or a differential severity of COPD in a subject, wherein said diagnosis of COPD, defined risk stratification, defined disease outcome, defined disease prognosis, or a differential severity of COPD in a subject is given if the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is above or below a certain cut-off value.

The method for the diagnosis of COPD according to claim 1, in a subject that has or is suspected to have COPD, comprising the steps of, in a sample of said subject: determining the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 158 or fragments thereof in a sample taken from said subject, wherein the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 158 or fragments thereof is correlated with the diagnosis of COPD in a subject,

wherein said diagnosis of COPD in a subject is given if the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is above or below a certain cut-off value.

The method according to claim 1 or 2, wherein the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof is preferably correlated with the said diagnosis of COPD, defined risk stratification, defined disease outcome, defined disease prognosis or a differential severity of COPD in a subject by a method which is selected from the following alternatives: (a) correlation with respect to the median of the level in an ensemble of pre-determined samples,

(b) correlation with respect to quantiles in an ensemble of pre-determined samples, and

(c) correlation with a mathematical model, such as for example Cox Regression.

The method according to any of the preceding claims, wherein the cut-off value of the level of one or more transcripts according to SEQ I D NO. 75 to SEQ I D NO. 233 or fragments thereof is about 1.5 fold (± 20%), 2 fold (± 20%), 3 fold (± 20%), 4 fold (± 20%) and most preferably 5 fold (± 20%) or more, higher or lower than the amount of the control sample, and may deviate depending on the patient analysed by about 5%, 8%, 10%, or 20%.

The method according to any of the preceding claims comprising the steps of

(a) determining the level of level of one or more transcripts according to SEQ ID NO. 75 to SEQ I D NO. 233 or fragments thereof; and

(b) correlating the level of one or more transcripts according to SEQ I D NO. 75 to SEQ I D NO.

233 or fragments thereof with a per quantile risk of COPD stability or declining condition of COPD.

The method for determining the differential severity of COPD in a subject according to any of the preceding claims, wherein the subject is under a COPD condition selected from the group of

comprising the steps of: determining the level of one or more transcripts according to SEQ ID NO. 204 to SEQ I D NO. 233 or fragments thereof in a sample taken from said subject, and (b) correlating the level of one or more transcripts according to SEQ I D NO. 204 to SEQ ID NO. 233 with said severity according to GOLD 1, GOLD 2, GOLD 3, or GOLD 4 and/or >80 FEVi %, 50-79 FEVi %, 30-49 FEVi %, <30 FEVi % or chronic respiratory failure.

A method of medical decision making for individual patient therapy related to the severity of the COPD disease by monitoring therapy response to a certain drug in a subject with sepsis or a sepsis like disease, comprising the steps of (a) - (c):

(a) providing for at least two samples from said subject at various time points selected from the group of

i. before initiation of therapy,

ii. after initiation of therapy, and/or

iii. at one or more further time point;

(b) determining the level of level of one or more transcripts according to SEQ ID NO. 1 to SEQ I D NO. 233 or fragments thereof in said sample;

(c) associating the level of level of one or more transcripts according to SEQ I D NO. 1 to SEQ ID NO. 233 or fragments thereof in said samples with a positive or a negative response to said certain drug; wherein said positive response is given if the level of one or more transcripts according to SEQ I D NO. 1 to SEQ ID NO. 233 or fragments thereof decreases or increases during drug treatment.

The method according to claim 7, further adapting therapy to the response to said drug.

A method for differentially diagnosing between COPD and another disease in a su bject, wherein the disease is either COPD or alternatively another disease selected from the group comprising (i) congestive heart failure, (ii) pulmonary embolism, (iii) pneumonia, (iii) pneumothorax, (iv) asthma, tuberculosis, (v) bronchopulmonary dysplasia and (vi) chronic bronchitis, comprising the steps of:

(a) determining the level of one or more transcripts according to SEQ I D NO. 75 to SEQ I D NO.

233 or fragments thereof in a sample taken from said subject, and

(b) correlating the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof to either (i) COPD or (ii) another disease, and wherein said correlation of the level of one or more transcripts according to SEQ ID NO. 75 to SEQ ID NO. 233 or fragments thereof to (i) COPD is given if the level is above or below a certain cutoff.

10. The method according to any of the preceding claims, wherein the sample is selected from a group comprising: a plasma sample, a serum sample, a whole blood sample, a blood sample or fractions thereof, a lymphatic fluid sample, a urine sample, a lung tissue, sputum, saliva, biopsy material, lymph nodes, urine, ejaculate, blood, blood serum, blood plasma, circulating tumour cells in blood or lymph, exosomes, micro vesicles, blood cells, leukocytes, monocytes, lymphocytes and neutrophils.

11. The method according to any of the preceding claims, wherein the sample is an isolated fraction of monocytes.

12. A method of risk stratification in COPD encompassing determining whether the patient has an inflammation of the lung comprising inflammatory granulocytes, such as eosinophils and/or neutrophils comprising:

(a) determining the level of one or more transcripts according to SEQ ID NO. 159 to SEQ ID NO.

202 or fragments thereof in a sample taken from said subject, and

202 or fragments thereof to either (i) COPD with inflammation and higher risk or (ii) COPD without inflammation and a lower risk, and wherein said correlation of the level of one or more transcripts according to SEQ ID NO. 159 to SEQ ID NO. 202 or fragments thereof to (i) COPD with inflammation is given if the level is above or below a certain cut-off.

13. The method according to claim 12, further comprising the step of

(c) adapting the therapy in so far as the inflammation is concerned.

A primer or probe nucleic acid or equivalent thereto that hybridizes under stringent conditions to a transcript or fragment of a nucleic acid transcript with a SEQ ID NO. 75 to SEQ ID NO. 233.

15. The primer or probe according to claim 14, wherein the primer or probe is selected from the group of SEQ ID NO. l to 74.

16. The primer or probe according to claim 15, wherein the primer or probe is labelled.

17. Use of a primer or probe according to any of claims 14 to 16 in a method according to claims 1 to 13.