WO2017158146A1 - Method for the diagnosis of chronic diseases based on monocyte transcriptome analysis - Google Patents

Abstract

The invention relates to a method for the diagnosis and/or prognosis of diseases, comprising (i) extracting monocytes from a blood sample; (ii) extracting nucleic acids from said monocytes; (iii) analysing the nucleic acids extracted from the monocytes for the presence or absence or increase or decrease of a biomarker, compared to a sample of a healthy person; designating the sample as positive, if the presence or absence of a biomarker is detected or depending on the expression level of the biomarker estimate the likelihood of improvement of the patient's health.

Description

Method for the diagnosis of chronic diseases based on monocyte transcriptome analysis Field of the invention

The present invention is in the field of medicine and diagnostics, in particular in the field of diagnosis and prognosis of chronic diseases. The invention also relates to the use of monocytes and the transcriptome of monocytes for the diagnosis of diseases, in particular chronic diseases. Background

Chronic diseases constitute a major cause of mortality and the World Health Organization (WHO) reports chronic non-communicable conditions to be by far the leading cause of mortality in the world, representing 35 million deaths in 2005 and over 60% of all deaths.

A growing body of evidence supports that prevention is effective in reducing the effect of chronic conditions; in particular, early detection results in less severe outcomes. Clinical preventive services include screening for the existence of the disease or predisposition to its development, counseling and immunizations against infectious agents. Despite their effectiveness, the utilization of preventive services is typically lower than for regular medical services. In contrast to their apparent cost in time and money, the benefits of preventive services are not directly perceived by patient because their effects are on the long term or might be greater for society as a whole than at the individual level.

Therefore, public health programs are important in educating the public, and promoting healthy lifestyles and awareness about chronic diseases. While those programs can benefit from funding at different levels (state, federal, private) their implementation is mostly in charge of local agencies and community-based organizations.

Studies have shown that public health programs are effective in reducing mortality rates associated to cardiovascular disease, diabetes and cancer, but the results are somewhat heterogeneous depending on the type of condition and the type of programs involved. For example, results from different approaches in cancer prevention and screening depended highly on the type of cancer. The rising number of patient with chronic diseases has renewed the interest in prevention and its potential role in helping control costs. A report from the Trust for America's Health suggests that investing $10 per person annually in community-based programs of proven effectiveness and promoting healthy lifestyle (increase in physical activity, healthier diet and preventing tobacco use) could save more than $16 billion annually within 5 years. In the European Union, approximately 70% to 80% of health care budgets across the EU are spent on treating chronic diseases. There is a wealth of knowledge within EU Member States on effective and efficient ways to prevent and manage cardiovascular disease, stroke and type-2 diabetes. There is great potential to reduce the burden of chronic disease by making better use of this knowledge. JA-CH ODIS, a European collaboration across member states, has been designed to exploit this potential through identifying, validating, exchanging and disseminating good practice on chronic diseases.

As such there is a need for suitable biomarkers for the diagnosis of chronic diseases in early stages.

Brief description of the Invention

The Inventors surprisingly found that the transcriptome of monocytes comprises biomarkers for the diagnosis and prognosis of chronic diseases such as COPD. In a first aspect of the invention, the invention therefore relates to a method for diagnosis and/or prognosis of chronic diseases. In particular, the invention relates to a method for the diagnosis and/or prognosis of diseases, comprising

(i) extracting monocytes from a blood sample;

(ii) extracting nucleic acids from said monocytes;

(iii) analysing the nucleic acids extracted from the monocytes for the presence or absence or increase or decrease of a biomarker, compared to a sample of a healthy person; designating the sample as positive, if the presence or absence of a biomarker is detected or depending on the expression level of the biomarker estimate the likelihood of improvement of the patient's health.

The Invention further relates to the use of a nucleic acid extract from monocytes for the diagnosis and/or prognosis of diseases, in particular chronic diseases. Definitions

Chronic diseases within the meaning of the present invention are long lasting or persistent diseases. Usually a chronic disease lasts for at least three months, if not longer, or even for a lifetime. Non-limiting examples for chronic diseases include:

• Alzheimer's disease

• chronic obstructive pulmonary disorder (COPD)

• diabetes

· rheumatoid arthritis

• osteoporosis

• HIV/AIDS

Chronic diseases are to be distinguished from acute diseases or recurring diseases, which relapse repeatedly with periods of remission in between.

Monocytes are the largest type of white blood cells (leucoplasts). They are part of the innate immune system of vertebrates including all mammals (humans included), birds, reptiles, and fish. They are amoeboid in shape, having a granulated cytoplasm. Monocytes have unilobar nuclei, which makes them one of the types of mononuclear leukocytes (containing azurophil granules).

Monocytes constitute 2% to 10% of all leukocytes in the human body. They play multiple roles in immune function. Such roles include: (1) replenishing resident macrophages under normal states, and (2) in response to inflammation signals, monocytes can move quickly (approx. 8-12 hours) to sites of infection in the tissues and divide/differentiate into macrophages and dendritic cells to elicit an immune response.

Monocytes are produced by the bone marrow from precursors called monoblasts, bipotent cells that differentiated from hematopoietic stem cells. Monocytes circulate in the bloodstream for about one to three days and then typically move into tissues throughout the body. They constitute between three to eight percent of the leukocytes in the blood. Half of them are stored as a reserve in the spleen in clusters in the red pulp's Cords of Billroth. In the tissues, monocytes mature into different types of macrophages at different anatomical locations. Monocytes are the largest corpuscles in the blood. There are at least three types of monocytes in human blood: the classical monocyte is characterized by high level expression of the CD14 cell surface receptor (CD14++CD16- monocyte);

- the non-classical monocyte shows low level expression of CD14 and additional co- expression of the CD16 receptor (CD14+CD16++ monocyte);

the intermediate monocyte with high level expression of CD14 and low level expression of CD16 (CD14++CD16+ monocytes). In the context of the present invention, the terms "threshold", "threshold value", "cut-off" and "cut-off value" are used synonymously.

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.

In the context of the present invention, a specific class of nucleic acid may be, inter alia, RNA, DNA, cDNA (complementary DNA), LNA (locked nucleic acid), mRNA (messenger RNA), mtRNA (mitochondrial), rRNA (ribosomal RNA), tRNA (transfer RNA), nRNA (nuclear RNA), siRNA (short interfering RNA), snRNA (small nuclear RNA), snoRNA (small nucleolar RNA), scaRNA (Small Cajal Body specific RNA), microRNA, dsRNA (double-stranded RNA), ribozyme, riboswitch, viral RNA, dsDNA (double-stranded DNA), ssDNA (single- stranded DNA), plasmid DNA, cosmid DNA, chromosomal DNA, viral DNA, mtDNA (mitochondrial DNA), nDNA (nuclear DNA), snDNA (small nuclear DNA) or the like or any other class or sub-class of nucleic acid which is distinguishable from the bulk nucleic acid in a sample.

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 mRNA transcript. A gene may result in different mRNA forms. These are encompassed by the term fragment.

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.

The term "biomarker" (biological marker) relates to measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc. Furthermore, a biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. A biomarker may be measured on a biosample (as a blood, urine, or tissue test), it may be a recording obtained from a person (blood pressure, ECG, or Holter), or it may be an imaging test (echocardiogram or CT scan) (Vasan et al. 2006, Circulation 113:2335- 2362). Biomarkers can indicate a variety of health or disease characteristics, including the level or type of exposure to an environmental factor, genetic susceptibility, genetic responses to exposures, biomarkers of subclinical or clinical disease, or indicators of response to therapy. Thus, a simplistic way to think of biomarkers is as indicators of disease trait (risk factor or risk biomarker), disease state (preclinical or clinical), or disease rate (progression). Accordingly, biomarkers can be classified as antecedent biomarkers (identifying the risk of developing an illness), screening biomarkers (screening for subclinical disease), diagnostic biomarkers (recognizing overt disease), staging biomarkers (categorizing disease severity), or prognostic biomarkers (predicting future disease course, including recurrence and response to therapy, and monitoring efficacy of therapy). Biomarkers may also serve as surrogate end points. A surrogate end point is one that can be used as an outcome in clinical trials to evaluate safety and effectiveness of therapies in lieu of measurement of the true outcome of interest. The underlying principle is that alterations in the surrogate end point track closely with changes in the outcome of interest. Surrogate end points have the advantage that they may be gathered in a shorter time frame and with less expense than end points such as morbidity and mortality, which require large clinical trials for evaluation. Additional values of surrogate end points include the fact that they are closer to the exposure/intervention of interest and may be easier to relate causally than more distant clinical events. An important disadvantage of surrogate end points is that if clinical outcome of interest is influenced by numerous factors (in addition to the surrogate end point), residual confounding may reduce the validity of the surrogate end point. It has been suggested that the validity of a surrogate end point is greater if it can explain at least 50% of the effect of an exposure or intervention on the outcome of interest. For instance, a biomarker may be a protein, peptide or a nucleic acid molecule. In the context of the present invention a biomarker is a nucleic acid molecule.

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 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.

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, preferably a chronic disease, at any stage of its development, and also includes the determination of predisposition of a subject to develop the disease.

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. 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.

Detailed description of the invention

The present invention relates to a method for the diagnosis and/or prognosis of diseases, comprising

(i) extracting monocytes from a blood sample;

(ii) extracting nucleic acids from said monocytes;

(iii) analysing the nucleic acids extracted from the monocytes for the presence or absence or increase or decrease of a biomarker, compared to a sample of a healthy person; designating the sample as positive, if the presence or absence of a biomarker is detected or depending on the expression level of the biomarker estimate the likelihood of improvement of the patient's health.

In one embodiment, the biomarker cannot be detected directly in a whole blood sample.

In one embodiment of the invention the method involves an expression analysis of the nucleic acids. In an alternative embodiment, the method involves the analysis whether or not a particular nucleic acid sequence is present or nor. In an alternative embodiment, the method involves the analysis of the expression level of a nucleic acid biomarker, in particular, if the expression level is increased or decreased compared to a healthy sample.

In an alternative embodiment, the analysis involves screening for a polymorphism as a biomarker.

In one embodiment of the invention the disease is a chronic disease. In a preferred embodiment, the disease is a chronic inflammatory disease. In an alternative preferred embodiment, the disease is a chronic lung disease. In an even more preferred embodiment the disease is a chronic inflammatory lung disease. In the most preferred embodiment the disease is COPD.

The inventors found, that chronic diseases, such as COPD, cannot be diagnosed using the transcriptome or a nucleic acid extract of whole blood samples. While an increase of monocytes in whole blood might be indicative of a disease, the specific diagnostic value is low. However, the inventors found, that chronic diseases could be diagnosed in early stages with the analysis of the transcriptome of monocytes.

The method according to the invention involves comparing the level of a 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 75th, 90th, 95th or 99th 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 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. Preferably, the biomarker is an RNA or DNA biomarker. More preferably, the biomarker is an RNA biomarker.

The monocytes can be extracted in any way suitable. The transcriptome may be extracted using any suitable method. The transcriptome itself might be analysed by any suitable method. In one embodiment, the transcriptome is analysed using sequencing techniques, such as next generation sequencing. In an alternative method, the transcriptome is analysed using microarrays. In an alternative embodiment, the transcriptome is analysed using a probe based assay. In a further alternative embodiment, the transcriptome is analysed using q T-PC .

Preferably, the assay used allows the quantification of the biomarkers in order to utilize the biomarker level for additional prognostic purposes. The invention preferably relates to a method wherein the level of one or more transcripts of a biomarker or fragments thereof isolated from monocytes is preferably correlated with the said diagnosis of a chronic disease, defined risk stratification, defined disease outcome, defined disease prognosis, or a differential severity of a chronic disease 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.

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 a bout 0.33 or less, still more prefera bly at least about 4 or more or about 0.25 or less, even more prefera bly at least about 5 or more or a bout 0.2 or less, and most prefera bly 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 a bout 1.1 or more or about 0.91 or less, more preferably at least about 1.25 or more or about 0.8 or less, still more preferably at least about 1.5 or more or about 0.67 or less, even more prefera bly at least about 2 or more or about 0.5 or less, and most prefera bly at least about 2.5 or more or about 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 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 level of one or more transcripts 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 the amount of the control sample, and may deviate depending on the patient analysed by about 5%, 8%, 10%, or 20%.

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.

Preferably, the sample is normalized on the level of a basal gene. Alternatively, the sample is normalized using an internal standard.

The inventors surprisingly found that the transcriptome of monocyte, in contrast to a blood analysis allows the differential diagnosis of chronic diseases, which might replace a more challenging analysis of other bodily fluids, such as for example sputum or puss. The invention in particular relates to:

• the isolation of blood monocytes in combination with the measurement of transcriptomes, single NA biomarkers, or RNA biomarker signatures thereof by using suitable methods including RNA next-generation sequencing, DNA microarray analyses, or RT-qPCR as a novel method for the diagnosis and prognosis of chronic diseases, chronic lung diseases, or COPD, • the isolation of blood monocytes in combination with the measurement of transcriptomes, single NA biomarkers, or RNA biomarker signatures thereof by using suitable methods including RNA next-generation sequencing, DNA microarray analyses, or RT-qPCR as a novel method for the prediction of the inflammatory status of organs in general, or the lung in particular,

• the measurement of transcriptomes of sputum samples by using suitable methods including RNA next-generation sequencing, DNA microarray analyses, or RT-qPCR as a novel method for the diagnosis and prognosis of chronic lung diseases or COPD,

• sets of RNA biomarkers derived from blood monocytes for the use in diagnostic and prognostic assays for chronic diseases, chronic lung diseases, or COPD,

• single RNA biomarkers or sets of RNA biomarkers derived from blood monocytes for the use in diagnostic assays indicating the inflammatory status of organs in general, or the lung in particular,

• sets of RNA biomarkers derived from sputum for the use in diagnostic and prognostic assays for COPD.

Measurement of RNA biomarkers can be done by any method suited to specifically estimate RNA levels, e.g. PCR-based methods like qRT-PCR, DNA microarrays, or next-generation sequencing. The assays can be applied for diagnosis of COPD, for identifying the severity of the disease, predicting its aggressiveness (prognosis), and/or for aiding the choice of therapy.

The invention further relates to the use of nucleic acids extracted from monocytes extracted from a sample of a patient in a method for the diagnosis of diseases, preferably chronic diseases.

The invention additionally relates to the use of the transcriptome of monocytes extracted from a sample of a patient in a method for the diagnosis of diseases, preferably chronic diseases.

The invention also relates to a kit comprising at least one probe or primer for the detection of a biomarker and reagents for the extraction of nucleic acids from monocytes. Examples

COPD biomarker from the monocyte transcriptome Description of the Cohort

For a cohort of 90 COPD patients of disease severity grades GOLD l-IV and 60 control subjects (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 (Peqlab). 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 visualized 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.

Results

The data demonstrates 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.

Data presented in Tables 1 and 2 also show that no differential regulation of biomarkers can be measured using whole blood or blood lymphocytes.

Table 1: Preferred probes for diagnosing COPD using whole blood

Table 2: Preferred probes for diagnosing COPD using blood monocytes

Table 1 shows whole blood samples. Here, no significant differences in gene expression can be observed, i.e. no differential regulation of biomarkers is measured. The false discovery rates (FDR) are always higher than 0.05 for the " best" candidate. Table 2 shows experiments with monocyte samples. Here, over 1107 statistically significantly expressed genes are measured with a local FDR less than 0.05 (see Table 2).

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.

The Inventors found that the following probes can be used as suitable biomarkers for differential diagnostic of COPD in a patient (see Figures 1 to 6).

Seq ID 1 A_21_P0001647

ACTGTATAACCTAAGAGTCGGGTGCAGGCTTGATAAAGAATTGGAAGCCCTCTGCAAAAC Seq ID 2 A_21_P0002437

GGCAGAGCATCTGTAGTGGAGAGAATGGGA TAGAGGAACGCTGAAAATAAAACAAGA 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

The probes above bind to sequences originating from the Loci characterized by SEQ ID NO 75 to SEQ ID NO 233. Any alternative probe binding to any of those sequences would therefore be equally suitable. Alternatively, any primer detecting said loci would be suitable in a qRT-PCR reaction. The different biomarkers allow a differential diagnostic, whether or not the patient has COPD (See Figures 1 to 4) or the progression state if COPD (Figures 5 to 6). So far, this diagnosis was only possible in sputum, which was a technical challenge for medical practitioners. With respect to COPD biomarkers noted above the inventors found that an up regulation of RNA fragments detectable by SEQ ID NO 1 to 35 in microsomes is indicative for a COPD diagnosis, while an up- regulation of RNA detectable by probes with SEQ ID NO 52 to 74 is suitable for differential diagnostics of COPD according to the GOLD standard. The up- or down regulation of the RNAs detectable by probes with SEQ ID NO 36 to 51 is suitable for the diagnosis of inflammatory COPD, eosinophilic inflammation or neutrophilic inflammation (see Tables 3- 5).

Table 3: Diagnosis of inflammatory COPD by SEQ ID NO 36 to 51

46 1

47 -1

48 -1

49 -1

50 1

51 -1

Table 5: Diagnosis of neutrophilic inflammation

Figure Legends

Figures 1 to 51 show diagnostic sensitivity of several biomarkers for COPD. Figures 52 to 74 show biomarkers for differential diagnosis of different stages of COPD.

Figure 75 shows expression intensities of probes in lymphocytes. Boxplots depict array expression intensities in log2 scale (y-axis) and are separated with respect to healthy and COPD samples (x-axis). Each plot refers to a single probe of SEQ ID NO.

Figure 76 shows expression intensities of probes in monocytes. Boxplots depict array expression intensities in log2 scale (y-axis) and are separated with respect to healthy and COPD samples (x-axis). Each plot refers to a single probe of SEQ ID NO. Figure 77 shows expression intensities of probes in whole blood. Boxplots depict array expression intensities in log2 scale (y-axis) and are separated with respect to healthy and COPD samples (x-axis). Each plot refers to a single probe of SEQ ID NO.

Claims

Claims

1. A method for the diagnosis and/or prognosis of diseases, comprising

(i) extracting monocytes from a blood sample;

(ii) extracting nucleic acids from said monocytes;

(iii) analysing the nucleic acids extracted from the monocytes for the presence or absence or increase or decrease of a biomarker, compared to a sample of a healthy person.

2. The method according to claim 1, wherein the biomarker is a DNA or NA biomarker.

3. The method according to any of claims 1 or 2, wherein the disease is a chronic disease.

4. The method according to any of claims 1 to 3, wherein the disease is a chronic inflammatory disease.

5. The method according to any of claims 1 to 4, wherein the disease is a chronic lung disease.

6. The method according to any of claims 1 to 5, wherein the disease is COPD.

7. Use of nucleic acids extracted from monocytes in a method for the diagnosis of diseases.

8. Use according to claim 7, wherein the disease is a chronic disease.

9. Kit for a method according to claims 1 to 6 or a use according to claims 7 or 8 comprising at least one probe or primer for the detection of a biomarker and reagents for the extraction of nucleic acids from monocytes.