WO2016148593A1

WO2016148593A1 - A microrna profile combined with a profile of blood protein markers as a test for the detection of lung cancer

Info

Publication number: WO2016148593A1
Application number: PCT/PL2016/000028
Authority: WO
Inventors: Rafał DZIADZIUSZKO; Witold RZYMAN; Ewa Szutowicz-Zielińska; Jacek Jassem; Joanna POLAŃSKA; Piotr WIDŁAK
Original assignee: Gdański Uniwersytet Medyczny
Priority date: 2015-03-13
Filing date: 2016-03-11
Publication date: 2016-09-22
Also published as: PL411561A1

Abstract

The object of the invention is a predictive method for the detection and/or exclusion of lung cancer which involves the measurement of expression levels of miRNA and the concentrations of proteins in the test sample, involving: — Examination, in the biological blood sample, of the 23 miRNAs and 6 protein markers included in Table 11. — Determination of the expression levels of indicated miRNAs and of the concentrations of indicated proteins in the blood relative to the expression levels of indicated miRNAs, by calculating the difference between the reference miRNA level and the measured value of expression of each of the indicated miRNAs. — Determination of protein concentrations and their comparison with the respective reference value given as the value of the upper limit of reference for a given protein marker in the population of healthy individuals, with which its serum concentration is compared. Furthermore, an object of the invention is also the test kit and the use of this method for the detection of lung cancer in individuals at high risk of lung cancer.

Description

A microRNA profile combined with a profile of blood protein markers as a test for the detection of lung cancer

Background of the invention The object of the invention is a combined signature consisting of microRNA and protein markers determined in the blood by EUSA that serves to predict which individual among individuals at high risk of lung cancer is significantly more likely to have this disease and, at the same time, serves to rule out the presence of lung cancer.

Context of the invention

Treatment of cancer is the greatest challenge for public healthcare in industrialised countries worldwide. Lung cancer is the leading cause of death due to cancer and accounted for 18.2% of deaths due to cancer in 2008. This is affected by late diagnosis and limited treatment options in 75% of the patients in whom the disease is diagnosed at a very advanced stage. Due to this fact the 5-year survival rate in highly developed countries reaches only nearly 15% of all lung cancer patients. According to the National Cancer Registry {Krajowy Rejestr Nowotworow), in 2006, in Poland, a total of 20,232 new cases of lung cancer were recorded, including 15,157 cases in men and 5,075 cases in women (which accounted for 23.6% and 8.2% of all cancers, respectively). The standardised rates per 100,000 persons in 2003 were 58.5 in men and 15.1 in women. Lung cancer is the cause of the greatest number of deaths due to cancer. In 2006, a total of 21.731 deaths were recorded, including 16,623 deaths in men and 5,108 deaths in women (respective percentages: 32.1% and 12.8% of all cancers). The standardised mortality rates per 100,000 persons in 2006 were 63.6 in men and 14.5 in women.

The constantly increasing number of new cases and the unsatisfactory treatment outcomes are a significant societal problem. Primary prevention, which involves a complete elimination of exposure to the components of tobacco smoke, is critical to reducing mortality due to lung cancer. The results of the attempts to implement various forms of primary prevention are, however, very limited. Secondary prevention, which involves the introduction of screening to identify affected individuals at an early stage of the disease, is the second most effective tool in the fight against !ung cancer. All the attempts to introduce a method that would meet all the requirements qualifying it as a commonly used screening tool have been unsuccessful so far.

In 2012, the results of the National Lung Screening Trial were published. The study was conducted in more than 53,000 volunteers and showed a reduction in mortality of more than 20% in the group of individuals at high risk of lung cancer among the volunteers who had undergone screening with low-dose computed tomography (LDCT) compared to the subjects monitored with the traditional X-ray. The analysis of this study showed that the detectability of lung cancer using LDCT was 2.4% within 3 years after performing three scans in each of the subjects. The study showed that the positive predictive value (PPV) was 1.2% and the negative predictive value (NPV) was 100%. The study was conducted in a group of individuals selected according to the risks defined as age 55-79 years and total exposure of more than 30 pack-years. The relatively high percentage of false positive results renders the introduction of this method as a routine tool for population screening rather questionable, both due to the high costs of discovering the cancer and due to the risks associated with performing invasive diagnostic tests in the group of patients diagnosed with a lung tumour. In view of the above, LDCT cannot be used at present as a screening tool for the entire population.

It is therefore needed to develop an effective, minimally invasive molecular test that would allow us to detect early, preclinical forms of lung cancer. A biological diagnostic test (a biomarker) determined in the blood meets these criteria and could considerably contribute to the improvement of treatment outcomes in lung cancer, as such test could be used as a screening test in the high-risk group defined above. The biomarkers that are currently being investigated most extensively as potential diagnostic tests for the detection of early lung cancer include circulating protein antibodies, microRNA (miRNA), and the proteomic profile as a separate or a multi- component peptide panel.

Summary of the invention

The object of the invention is a new test that allows one to determine who in a group of individuals at high risk of lung cancer is most likely to have the disease. The proposed test is employed as an independent method used before the commencement of screening with LDCT. This method involves a combined measurement of the expression of selected microRNAs and the levels of selected protein markers used as a multi-component profile. Thanks to its high negative predictive value (NPV), the test allows one to rule out lung cancer, at an accuracy of more than 95%, in an individual with a negative result. On the other hand, thanks to its relatively high positive predictive value (PPV), the test allows one to determine, at the probability of 35%, whether a given individual has lung cancer or not.

This profile can also be used in high-risk individuals as a predictor indicating individuals who should undergo imaging studies to facilitate the decision regarding their further evaluation and treatment.

Currently, LDCT is the only lung cancer screening test of proved clinical usefulness. This test is characterised by a very high NPV (100%) that allows one to rule out the cancer in an individual with a negative test result, and by a very low PPV (approx. 1%). This means that the cancer will only be detected in 1 in 100 individuals tested with this method. This generates very high costs of detection of one case of the cancer, significant psychological problems for individuals diagnosed with a tumour which subsequently turns out not to be cancerous (false positive results) and the necessity for further follow-up or invasive diagnostic evaluation in individuals with a positive test result. These limitations are a considerable obstacle to the widespread use of LDCT. The method we have invented allows one to considerably narrow down the group of individuals with potential lung cancer. Detection of a biomarker identifying a lung cancer patient has always been a key task in molecular biology. Biomolecular markers may be assessed directly in tumour tissue specimens, serum samples or in samples of other body secretions, such as sputum or bronchia! secretions. MicroRNA, which is present both in the serum and plasma, has the properties that meet the criteria of a marker for the detection of cancer. MicroRNAs are short (19- to 23-nucleotide-long) RNA chains which play a regulatory role in the processes of transcription and translation. MicroRNAs bind with mRNA chains in their non-coding parts and block translation in ribosomal complexes. Due to the incomplete complementarity between the corresponding binding sequences, one microRNA molecule can control expression of hundreds, even thousands of mRNAs. Studies conducted in recent years have demonstrated high stability of miRNA in samples collected from tumours and from serum. They have also shown that the developing tumour may be associated with a specific miRNA signature even before it is clinically detected. Furthermore, serum, plasma or tissue samples may be tested within up to 10 years after collection. Shen et al. published an miRNA signature composed of high expression of miR-21 and miR-210, and low expression of miR-485— 5p obtained from the sera of patients in whom a pulmonary nodule had been detected by computed tomography. This signature enables differentiation of incidental pulmonary lesions into benign tumours and lung cancers. Boeri et al. showed that expression of miR-155, miR-197 and miR-182 was significantly elevated in lung cancer patients verus healthy controls. Bianchi et al. published a signature composed of miR- 15b and miR-27b as a signature enabling differentiation between individuals with lung cancer and healthy individuals at a high sensitivity and specificity exceeding 90%, based on examination of the serum from individuals undergoing screening in the group at high risk of lung cancer. Heegaard et al., based on serum testing, showed reduced levels of miR-146b, miR-221, miR-155, miR-17-5p, miR-27a and miR-106a in patients with lung cancer. Yuxia i wsp. investigated miRNA expression in the plasma and proposed a signature composed of elevated expression of miR-1254 and miR-574-5p to be used for the purpose of differentiating individuals with early lung cancer from healthy individuals, based on expression of miRNA in the serum. Many inventions have been made that define miRNA signatures used to determine the possibility of lung cancer being present or to predict the course of the disease. These inventions are, however, applicable for the most part in advanced stages of the cancer and most often involve miRNA testing in tissue samples — histopathological tumour specimens. The patent description WO 2010/139810 disclosed a method for diagnosing early lung cancer by determining the profile of miRNA levels in a biological sample (blood, plasma and/or serum) collected from the patient and by comparing the profile to the reference expression profile, which allows one to detect early lung cancer. The diagnosing involves examination of the level(s) of one or more miRNAs. Another international patent description, namely WO 2012/089630, discloses a method for diagnosing lung cancer in asymptomatic patients or for differentiating benign from malignant lung tumours, or for establishing the prognosis in lung cancer, through detection of miRNA from a list of 34 miRNAs in a biological sample. Comparison of miRNA levels in the test sample with the reference levels enables one to establish the diagnosis and prognosis in lung cancer or to differentiate benign from malignant iung tumours.

In the present invention, we present a test that includes a signature composed of 23 miRNAs (Table 11) which show altered expression in the plasma from individual with iung cancer relative to the expression of corresponding miRNAs in healthy individuals. The plasma we tested originated from the participants of the Pomeranian Pilot Programme for Lung Cancer Screening [Pomorski Pilotazowy Program Bada Przesiewowych Raka Pfuca). The sample had been collected in compliance with a strict protocol for the collection, preparation and storage of samples. The analysis was based on the examination of miRNA levels (expression) in plasma samples from 100 individuals diagnosed with early lung cancer and 300 healthy individuals, who comprised the control group. The determinations were performed by polymerase chain reaction preceded by the reverse transcription stage. The control group was sex- and age-matched and its members had been selected from among 3500 individuals considered healthy when the test was being performed. Protein markers may be assessed directly in the serum or plasma. There are several dozens of laboratory tests in the modern laboratory diagnostics which show various degrees of association with lung cancer in terms of test potency and test specificity. They are easy to perform and do not require expensive apparatus. Also, their costs are low compared to the costs of imaging and endoscopic studies. Their drawbacks as independent markers are, however, their low sensitivity and specificity. As a result, when a single marker is being measured, there is always a wide margin of diagnostic uncertainty, which is reflected by values of the diagnostic efficacy index, which does not exceed 0.70-0.75 for advanced stages of the disease.

Measurement of nearly all markers is currently possible with the use of ready-made kits that utilise various immunochemical methods. They include specific primary antibodies, mono- and/or polyclonal, that are mainly bound to the solid phase (test tube wall, glass microspheres) and enzyme-labelled detection antibodies that react with substrates yielding reaction products that can be determined using colorimetric, fluorometric or luminometric methods. These assays are available in various formats intended for automatic or manual determinations on measurement platforms (including EL!SA). This allows one to adapt these methods for the purpose of measurements of a wide range of concentrations of these analytes in the plasma/serum from 10^"8 to 10^'18 mol/l.

Based on the widely available literature 16 protein markers selected and investigated whose levels, through various/independent mechanisms, could be increased in a possibly large fraction of individuals with lung cancer. Based on a study of 100 patients with lung cancer and 300 healthy individuals a signature composed of 6 markers was identified for use to detect early lung cancer, with a particular emphasis on early forms of the disease. Of the various groups of tumour markers, the following were selected as potentially useful for the development of a diagnostic algorithm:

"Glycoprotein" antigens: Carcinoembryonic antigen (CAE) and/or CA 125 and/or CA 199. "Cytokeratin" and secretory antigens: CYFRA 21-1 or tissue polypeptide antigen (TPA). Both markers are equally valuable indicators of proliferation rate. Dickkopf-1 (D K1) is a secretory protein significantly correlated with lung tumours.

"Neuronal" antigens: Neuron-specific enolase (NSE) and/or squamous-cell carcinoma antigen (SCC-Ag), S110B protein— an antigen specific for squamous-cell and giant-cell lung carcinomas but not for lung adenocarcinomas or small-cell lung carcinomas.

Specific antigens: Progastrin-releasing peptide (ProGRP), a protein specific for small- cell lung carcinoma.

Accompanying antigens: C-reactive protein (CRP), alpha-1 antitrypsin, retinol-btnding protein 4 (RBP 4). a) Determination of the expression levels of specific miRNAs in the plasma.

Comparing the measured expression level with the reference expression defined on the basis of the median value of miRNA expression for the control sample.

In the present invention, we propose a signature composed of 23 miRNAs, which show altered expression in the plasma from individual with lung cancer relative to the reference expression defined on the basis of the median value of miRNA expression for the control sample, and 6 protein markers, whose serum concentrations in individuals with lung cancer are higher than those defined by relevant normal ranges or higher than the relevant upper cut-off levels. The plasma we tested originated from the participants of the Pomeranian Pilot Programme for Lung Cancer Screening [Pomorski Pilotazowy Program Bada Przesiewowych Raka Pfuca). The sample had been collected in compliance with a strict protocol for the collection, preparation and storage of samples. The analysis was based on the measurements of serum protein marker levels in 100 individuals diagnosed with early lung cancer and 300 healthy individuals, who comprised the control group. The determinations were performed by polymerase chain reaction preceded by the reverse transcription stage. The control group was sex- and age-matched and its members had been selected from among 3500 individuals considered healthy when the test was being performed. The object of the invention is a predictive method for the detection and/or exclusion of lung cancer which involves the measurement of expression levels of miR A and the concentrations of proteins in the test sample, involving:

a. Examination, in the biological blood sample, of the 23 miRNAs and 6 protein markers included in the following list:

ACAGUAGUCUGCACAUUGGUUA hsa-miR-199a-3p

UAGCUUAUCAGACUGAUGUUGA

hsa-miR-21

CACGCUCAUGCACACACCCACA

hsa-miR-574-3p

UUCACAGUGGCUAAGUUCCGC

hsa-miR-27a

UUCACAGUGGCUAAGUUCUGC

hsa-miR-27b

UAGCACCAUUUGAAAUCGGUUA

hsa-miR-29c

UCCCUGUCCUCCAGGAGCUCACG

hsa-miR-339-5p

GUGCAUUGUAGUUGCAUUGCA

hsa-miR-33a

UGAGGGGCAGAGAGCGAGACUUU

hsa-miR-423-5p

AUAUAAUACAACCUGCUAAGUG

hsa-miR-374b

AACAUAGAGGAAAUUCCACGU

hsa-miR-376c

UCUUCUCUGUUUUGGCCAUGUG

hsa-miR-942

Protein

DRG-cyfra21-l

Protein

IBL-hCRP

Protein

DRG-NSE Protein

27 NT-CEA

Protein

28 NT-CA125

Protein

29 USCN-SCCA1

b) Determination of the expression levels of indicated miRNAs and of the concentrations of indicated proteins in the blood relative to the expression levels of indicated miRNAs, by calculating the difference between the reference mtRNA level and the measured value of expression of each of the indicated miRNAs.

c) Determination of protein concentrations and their comparison with the respective reference value given as the value of the upper limit of reference for a given protein marker in the population of healthy individuals, with which its serum concentration is compared.

A method which involves the measurement of the levels of expression of the following miRNAs and of the concentrations of the following protein markers: IBL- hCRP, USCN-SCCA1, hsa-let-7a, hsa-let-7f, hsa-miR-122, hsa-miR-142-5p, hsa-miR-144, hsa-miR-148b, hsa-miR-21, hsa-miR-23b, hsa-miR-27a, hsa-miR-29c.

A method which involves the measurement of the levels of expression of the following miRNAs and of the concentrations of the following protein markers: USCN- SCCA1, IBL-hCRP, hsa-let-7f, hsa-miR-142-5p, hsa-miR-122.

A method where the biological samples are collected from peripheral blood.

A method where iung cancer is early lung cancer.

This method is used prior to an LDCT scan.

This method is used following an LDCT scan.

The use of a method for the detection of lung cancer in individuals at high risk of lung cancer. A test kit for the detection of lung cancer using the a combination method combining measurement of miRNA expression by PCR with measurement of blood concentrations of proteins by EL1SA, where:

— for miRNA, the kit contains (i) a reference level determined on the basis of a control kit, and (ii) a biological sample for the measurement of miRNA expression levels using PCR;

— for the proteins, the kit contains a reference value given as the value of the upper limit of reference for a given protein marker in the population of healthy individuals, with which its serum concentration is compared;

— the markers defined in Claim 1 are measured.

A kit that measures concentrations of the following protein markers and expression levels of the following miRNAs: IBL-hCRP, USCN-SCCA1, hsa-let-7a, hsa-let-7f, hsa-miR- 122, hsa-miR-142-5p, hsa-miR-144, hsa-miR-14Sb, hsa-miR-21, hsa-miR-23b, hsa-miR- 27a, hsa-miR-29c

A kit that measures concentrations of the following protein markers and expression levels of the following miRNAs: USCN-SCCA1, IBL-hCRP, hsa-tet-7f, hsa-miR-142-5p, hsa-miR-122.

The terms used in the patent description and the patent claims have the following meanings: individual at high risk of lung cancer— An asymptomatic individual aged 50-79 years who has smoked at least 20 pack-years of tobacco.

Pack-years (a pack-year) — A traditional measure of the risk of tobacco-related diseases used in medicine. The number of pack-years is calculated by multiplying the number of packs of cigarettes smoked per 24 hours by the number of years of smoking, e.g. 1 pack-year refers to smoking 1 pack of cigarettes (20 cigarettes per pack) for 1 year. Low-dose computed tomography (LDCT) scan of the chest— A CT technique that does not involve intravenous administration of a contrast agent but uses low exposure parameters (a voltage of 120 kVp, an intensity of 40-80 mA), to maximise radiological protection and minimise the absorbed dose of radiation, while preserving the diagnostic value and sensitivity. miRNA (microRNA)— A group of RNA molecules about 20 nucleotides long. They are involved in the regulation of gene expression mainly at the post-transcriptional level. This is possible thanks to their complementarity to the 3'UTR regions in mRNA. MicroRNAs (miRNAs) are very important for the normal development and functioning of the body, as they affect such processes as angiogenesis, apoptosis, cell cycle control, and carcinogenesis. Their significance is highlighted by the fact that more than 30% of human genes are controlled by miRNA. Interferences in miRNA expression may result in abnormal course of numerous intracellular processes. Such abnormalities are, for instance, observed in tumour cells, and signtures of miRNA expression are characteristic of individual tumour types. It is therefore suggested that miRNA may serve as diagnostic and prognostic factors in malignant tumours, including haematological malignancies, in which altered expression of specific miRNAs may suggest a mild or aggressive course of the disease, !t is also possible to estimate overall survival or time to treatment based on the miRNA expression profile. The identification of changes in miRNA levels in patients with haematological malignancies seems to facilitate the selection of treatment and offers the opportunity of personalised treatment selection. Recent reports suggest that the miRNAs present in the plasma or serum may also provide a good characterisation of specific haematological malignancies. This is of potentially considerable significance, given the availability of testing material, facilitation of diagnostic procedures and shortening of the time required to perform these procedures. mRNA (messenger RNA) — A type of ribonucleic acid (RNA) whose function is to transfer genetic information on the sequence of specific polypeptides from genes to the translational apparatus.Upon binding to ribosomes, the mRNA molecules serve as a matrix for the synthesis of polypeptides in which the subsequent triplets of mRNA nucleotides (the so-called codons) are recognised by corresponding fragments of tRNA (the so-called anticodons) which transport amino acids, thanks to which the translation process leads to the formation of the correct sequence of the peptide.

ELESA {enzyme-linked immunosorbent assay)— An assay used in biomedical studies, both research and diagnostic studies. It is used to detect specific proteins in the test material using polyclonal or monoclonal antibodies conjugated with an appropriate marker enzyme.

Positive predictive value (PPV)— The likelihood of having the disease by an individual with a positive test result, if the individual tests positive, the PPV provides the individual with information on how certain the he/she can be that he/she is suffering from a given disease.

TP+FP

TP - true positive value

FP - false positive value (1)

The confidence interval is constructed based on the Clopper-Pearson method for a single proportion.

Negative predictive value (NPV) — The likelihood of not having the disease by an individual with a negative test result. If the individual tests negative, the NPV provides the individual with information on how certain the he/she can be that he/she is not suffering from a given disease.

TN - true negative value

FN - false negative value (2)

Positive and negative predictive values depend on the prevalence of the disease {prevalence rate). Multiple random variation (MRV)— Also: Monte Carlo cross-validation. A method for the assessment of the performance of prediction and stability of a signature that involves multiple construction of the classifier discriminant function on the basis of two randomly created data subsets: the training set and the testing set. At each step, selected indicators of classification performance (NPV and PPV in this- case) are assessed, and the resulting set forms the basis for the estimation of the interval estimate of the indicator for the population. Subsequent draws of the training set and the testing set are independent from the previous draws and their structure (the percentages of sick individuals and healthy individuals) reflects the structure of the baseline data set. The percentage ratio p characterises the ratio of the size of the training set to the size of the testing set in each iteration.

Receiver operating characteristic (ROC) curve — A tool for the assessment of the performance of a classifier; it provides a combined description of the classifier's sensitivity and specificity. This method of decision support system is widely used in many applications, including medical diagnostics. Sensitivity (Sens)— Aiso: true positive rate. An indicator of classification performance that defines the proportion of false positive results in the group of sick individuals.

FN - false negative value (3)

Specificity (SPC)— Also: true negative rate. An indicator of classification performance that defines the proportion of negative results in the group of healthy individuals.

SPC = ™

FP - false positive value (4)

Area under curve (AUC)— The value of the area under the ROC curve. The area under the ROC curve is the probability that a classifier will give a higher rank to a randomly selected case from an appropriate group rather than give a higher rank to a randomly selected case from the group which is known not to include the data looked for. AUC contains a description of detection precision throughout the range of the system's operation. An AUC of 0.5 may be described as a random activity, and an AUC of 1.0 is the ideal indicator. This means that the curve running closer to the upper left corner represents a higher diagnostic accuracy.

Prediction method — A method that enables a rational, scientific prediction of the occurrence of an event. It is aiso a method for the prediction of the current status of a system, i.e. a method for the determination of the risk of the presence of an event and/or for ruling out the presence of an event.

Screening test— A type of strategic test which is conducted among individuals who do not have the symptoms of a specific disease in order to detect the disease, provide early treatment or prevent serious consequences of the disease in future.Screening tests are performed in the entire population or in the so-called high-risk groups. Screening tests are aimed at detecting a specific disease in its early phase, thanks to which early intervention is possible.

Control sample— The control in our study comprised individuals at high risk of lung cancer in whom the cancer was not detected during screening with LDCT

Early lung cancer— Asymptomatic lung cancer.

Molecular signature— A unique set of molecular features (here: combined features of miRNAs and protein markers) that are characteristic of lung cancer.

Reference !evel(in the method presented herein)— The median of the expression levels of 78 miRNAs, which averages C_re 7_8]=30.78; 95% CI: [30,68; 30,88]. When only expression levels of the indicated 23 miRNAs are being measured, then the median of the expression levels of the indicated 23 miRNAs corrected by 0,497; 95% CI: ???? may be adopted as the reference level. Description of the figures:

Fig. 1. Mean PPV and NPV values for a 29-item signature obtained using the logistic regression method combined with the MRV cross-validation technique, according to the threshold value (thr). The point with the suggested threshold value (thr) of 0.101 is marked red.

Fig. 2. An ROC curve for a 29-item signature of miRNAs and proteins constructed on the basis of estimations of the classifier's sensitivity and specificity carried out using the MRV cross-validation technique. The point with the suggested threshold value (thr) of 0.101 is marked red.

Fig. 3. Mean PPV and NPV values for a 5-item signature obtained using the logistic regression method combined with the forward feature selection algorithm, according to the threshold value (thr). The point with the suggested threshold value (thr) of 0.101 is marked red.

Fig. 4. An ROC curve for a 5-item signature of miRNAs and proteins constructed on the basis of estimations of the classifier's sensitivity and specificity carried out using the MRV cross-validation technique. The point with the suggested threshold value (thr) of 0.101 is marked red. Fig. 5. Mean PPV and NPV values for a 10-item signature obtained using the logistic regression method combined with the backward feature elimination algorithm, according to the threshold value (thr). The point with the suggested threshold value (thr) of 0.101 is marked red.

Fig. 6. An ROC curve for a 10-item signature of miRNAs and proteins constructed on the basis of estimations of the classifier's sensitivity and specificity carried out using the MRV cross-validation technique. The point with the suggested threshold value (thr) of 0.101 is marked red.

Fig. 7. The mean PPV and NPV values for a 12-item signature obtained from the combination of forward and backward signatures, according to the threshold value thr. The point with the suggested threshold value (thr) of 0.101 is marked red. Fig. 8. An ROC curve for a 12-item signature of miRNAs and proteins constructed on the basis of estimations of the classifier's sensitivity and specificity carried out using the MRV cross-validation technique. The point with the suggested threshold value {thr} of 0.101 is marked red.

Invention — The present invention is illustrated by the following examples of execution, which are not a limitation of the present invention in any way.

Example 1:

A. Collection, into an EDTA-containing tube, of a 10-ml sample of peripheral blood from an individual at high risk of lung cancer. Centrifugation at 600 g, at 4 degrees Celsius for 20 minutes. Transfer of plasma with a pipette into a new centrifuge tube. Repeat centrifugation at 1500 g, at 4 degrees Celsius for 15 minutes. Isolation of RNA using commercial kits for isolation (miRCURY RNA isolation kit - biofluids) in accordance with the manufacturer's isolation protocol. Reverse transcription using the commercial miRCURY RNA Universal RT kits.

Measurement of the levels of 78 miRNAs (Table 9) using miRCURY RNA Universal RT interpreted as the number of PCR reaction cycles C_p. Calculation of the relative levels of miRNAs defined as the difference between the reference value (defined as the median value of the expression levels of all the 78 miRNAs) designated C_ref and the measured level designated C_p. The relative value of expression of each of the miRNAs mentioned above forms the basis for calculation of the discriminant function in the model for predicting the occurrence of lung cancer. A value of the discriminant function exceeding the threshold value thr forms the basis for qualifying an individual to the group at high risk of lung cancer. B. Collection, into a white-top tube, of a 10-ml sample of peripheral blood from an individual at high risk of lung cancer. Incubation for 30 minutes at room temperature. Centrifugation of biood at 1000 g, at 18-20 degrees Celsius for 10 minutes. Collection of 6 aliquots of 500 μΙ of serum into cryogenic tubes and freezing at -80 degrees Celsius.

Measurement of the concentrations of 16 proteins using EL!SA. The value of expression of each of the miRNAs and proteins mentioned above forms the basis for calculation of the discriminant function in the model for predicting the occurrence of lung cancer. A value of the discriminant function exceeding the threshold value thr forms the basis for qualifying an individual to the group at high risk of lung cancer.

The discriminant function in the prediction model obtained using logistic regression is as follows:

where p(z) is the value of the discriminant function, and the value of the argument z is calculated as a linear combination of the relative values of expression levels of n selected miRNAs or proteins and is given by the following formula:

Z = βθ + β Χ + #2 2 + - + βη^Χ, (6) The maximum likelihood (ML) method was used to estimate the values of β, while the MRV multivariate cross-validation technique was the basis for the selection of features and estimation of the NPV and PPV values. The resulting signature consists of 29 miRNAs and proteins showing expression levels that allow one to differentiate between sick and healthy individuals. Their list and their contribution percentages β, are provided in Table 1. Table 2 provides the mean values of NPV, PPV, sensitivity (Sens), specificity (SPC) and AUC for three selected cut-off thresholds thr. Fig. 1 illustrates the dependence of the mean NPV and PPV values from the cut-off threshold thr, while Fig. 2 shows the ROC curve.

Table 1. miRNAs and proteins that make up the signature obtained using the logistic regression method using MRV cross-validation, and the related values of β,. miRNA or protein Estimation of β miRNA or protein Estimation of β

Constant

-5.36024 hsa-miR-339-5p 0.27719 component

DRG-cyfra21-l 0.06078 hsa-miR-29c -0.83521

IBL-hCRP 0.18896 hsa-miR-374b 0.07011

DRG-NSE -0.00093 hsa-miR-199a-3p 0.07137 hsa-miR-122 -0.35483 hsa-miR-27b 0.16525

NT-CEA 0.00441 hsa-miR-376c 0.00095 hsa-miR-33a -0.24650 hsa-miR-340 -0.14263 hsa-let-7f 0.09651 USCN-SCCA1 -0.24959 hsa-miR-142-5p -0.57883 hsa-miR-942 0.27531 hsa-miR-103 1.42606 hsa-miR-27a -0.50576

NT-CA125 0.03556 hsa-miR-21 2.19279 hsa-miR-17 -0.98622 hsa-miR-574-3p -0.12931 hsa-miR-14Sb -1.57559 hsa-miR-181a -0.11885 hsa-miR-107 -1.09687 hsa-miR-142-5p 0.75109 hsa-miR-142-3p -0.39206 hsa-miR-423-5p -0.24386

Table 2. Estimations of the values of PPV, NPV, sensitivity, specificity and AUC for a 29-item signature of miRNAs and proteins depending on the adopted threshold value thr and the method of error assessment.

Example 2:

Collection of blood samples and determination of the relative expression levels of individual miRNAs and concentrations of individual proteins are performed as described in Example 1. The discriminant function described by Equations (5) and (6) also remains unchanged. What is modified compared to Example 1 is the method for the selection of microRNAs and proteins, as the stage in which the classifier was constructed involved the use of logistic regression in combination w^'ft forward feature selection (FS) and with Bayesian information criterion (BIC) of model selection. The final signature is composed of 5 miRNAs and proteins (which are a subset of the initial set of 29 features given in Table 9), and the obtained estimations of PPV and NPV still fall within the intervals that meet the criteria for diagnostic and predictive efficacy.

The list of miRNAs and proteins making up the signature and their contribution percentages ¾ are provided in Table 3. The mean values of NPV, PPV, sensitivity (Sens), specificity (SPC) and AUC for three selected cut-off thresholds thr are provided in Table 4. Fig. 3 illustrates the dependence of the mean NPV and PPV values from the cut-off threshold thr, while Fig. 4 shows the ROC curve.

Table 3. miRNAs and proteins that make up the signature obtained using the logistic regression method using forward feature selection (FS) and the related values οίβ. miRNA or protein Estimation of β

Constant

-1.769

component

USCN-SCCA1 -0.212

IBL-hCRP 0.177

hsa-let-7f 0.556

hsa-miR-142-5p -0.971

hsa-miR-122 -0.339

Table 4. Estimations of the values of PPV, NPV, sensitivity, specificity and AUC for a 5-item signature of miRNAs and proteins depending on the adopted threshold value thr and the method of error assessment.

Example 3; Collection of blood samples and determination of the relative expression levels of individual miRNAs and concentrations of individual proteins are performed as described in Example 1. The discriminant function described by Equations (5) and (6) also remains unchanged. What is modified compared to Example 1 is the method for the selection of microRNAs and proteins, as the stage in which the classifier was constructed involved the use of logistic regression in combination with backward feature elimination (BE) and with Bayesian information criterion (BIC) of model selection. The final signature is composed of 10 miRNAs and proteins (which are a subset of the initial set of 29 miRNAs given in Table 9), and the obtained estimations of PPV and NPV still fall within the intervals that meet the criteria for diagnostic and predictive efficacy.

The list of miRNAs and proteins making up the signature and their contribution percentages ¼ are provided in Table 5. The mean values of PV, PPV, sensitivity (Sens), specificity (SPC) and AUC for three selected cut-off thresholds thr are provided in Table 6. Fig. 5 illustrates the dependence of the mean NPV and PPV values from the cut-off threshold thr, while Fig. 6 shows the ROC curve. Table 5. miRNAs and proteins that make up the signature obtained using the logistic regression method using backward feature elimination (BE) and the related values of β,.

Table 6. Estimations of the values of PPV, NPV, sensitivity, specificity and AUC for a 10-item signature of miRNAs and proteins depending on the adopted threshold value thr and the method of error assessment.

Example 4:

Collection of blood samples and determination of the relative expression levels of individual miRNAs and concentrations of individual proteins are performed as described in Example 1. The discriminant function described by Equations (5) and (6) also remains unchanged. What is modified compared to Example 1 is the method for the selection of miRNAs and proteins. In this example, the FS signature (Example 2) was combined with the BE signature (Example 3), as a result of which the final signature is composed of 12 miRNAs and proteins (which are a subset of the initial set of 29 features given in Table 9) and the obtained estimations of PPV and NPV still fall within the intervals that meet the criteria for diagnostic and predictive efficacy. The list of miRNAs and proteins making up the signature and their contribution percentages ¾· are provided in Table 7. The mean values of NPV, PPV, sensitivity (Sens), specificity (SPC) and AUC for three selected cut-off thresholds thr are provided in Table 8. Fig. 7 illustrates the dependence of the mean NPV and PPV values from the cut-off threshold thr, while Fig. 8 shows the ROC curve. Table 7. miRNAs and proteins that make up the signature obtained using the logistic regression method and the combined FS and BE signatures, and their related values of fi. miRNA or protein Estimation of β

Constant

-4.225

component

lBL-hCRP 0.189

USCN-SCCA1 -0.215

hsa-let-7f -1.691

hsa-let-7f 0.835

hsa-miR-122 -0.405

hsa-miR-142-5p -0.705

hsa-miR-142-5p 0.885

hsa-miR-148b -2.089

hsa-miR-21 1.896

hsa-miR-23b 1.814 miRNA or protein Estimation of β

hsa-miR-27a -1.295

hsa-miR-29c -1.047

Table 8. Estimations of the values of PPV, NPV, sensitivity, specificity and AUC for a 12-item signature of miRNAs and proteins depending on the adopted threshold value thr and the method of error assessment.

Table 9. List of 78 miRNAs:

ii miRNA name #¾ miRNA name II miRNA name

1 hsa-let-7a 27 hsa-miR-185 53 hsa-miR-339-5p

2 hsa-let-7b 28 hsa-miR-18a 54 hsa-miR-33a

3 hsa-let-7c 29 hsa-miR-199a-3p 55 hsa-miR-340

4 hsa-let-7d_st 30 hsa-miR-20a 56 hsa-mtR-365

5 hsa-let-7f 31 hsa-miR-20b 57 hsa-miR-373__st

6 hsa-miR-103 32 hsa-miR-21 58 hsa-miR-374a

7 hsa-miR-106a 33 hsa-miR-221 59 hsa-miR-374b

8 hsa-miR-106b 34 hsa-miR-223 60 hsa-miR-376b

9 hsa-miR-107 35 hsa-miR-23a 61 hsa-miR-376c

10 hsa-miR-122 36 hsa-miR-23b 62 hsa-miR-378

11 hsa-miR-127-3p 37 hsa-miR-25 63 hsa-miR-423-3p

12 hsa-miR-130b 38 hsa-miR-27a 64 hsa-miR-423-5p

13 hsa-miR-140-3p 39 hsa-miR-27b 65 hsa-miR-425

14 hsa-miR-142-3p 40 hsa-miR-28-3p 66 hsa-miR-425_st ^■

15 hsa-miR-142-5p 41 hsa-miR-28-5p 67 hsa-miR-451

Analytical procedures

A. The technology of quantitative real-time PCR preceded by the RNA isolation and reverse transcription stages was used to determine the plasma level of miRNA. The individual stages of assessment of miRNA levels in the peripheral blood plasma adopted in the present patent application were as follows:

1. Collection of 10-ml peripheral blood samples into EDTA-containing test tubes.

2. Centrifugation at 600 g, at 4 degrees Celsius for 20 minutes.

3. Transfer of plasma with a pipette into a new centrifuge tube.

4. Repeat centrifugation at 1500 g, at 4 degrees Celsius for 15 minutes. .

5. Isolation of RNA using commercial kits for isolation (miRCURY RNA isolation kit - biofluids) in accordance with the manufacturer's isolation protocol.

6. Reverse transcription using the commercial miRCURY RNA Universal RT kits.

7. Measurement of the levels of 78 miRNAs using miRCURY RNA Universal RT.

The results were presented as raw data (number of qPCR reaction cycles after which the threshold number of transcripts were obtained in the amplification reaction) and data normalised to the median miRNA level. B. Serum levels of all the potential predictive and diagnostic markers were determined using ELiSA microplates (96-well plates) on the automated measurement platform ETI- MAX 3000 (Dia Sorin, Bellugia, Italy). The principle of determination for each parameter was similar, it involved the binding of the analyte present in the tested serum sample with a specific primary monoclonal antibody absorbed on the surfaces of the microplate wells. After completion of binding the unbound proteins were removed by washing. A second antibody — a horseradish peroxidase-!abelled polyclonal antibody to human serum proteins— was then added. After the antibody had been bound with the absorbed analyte the samples were washed and a tetramethylbenzidine solution (the peroxidase substrate) was added; tetramethylbenzidine underwent oxidation yielding a colour product; incubation was interrupted after 30-60 minutes using H2SO4 and the absorbance value in individual welis was recorded. The concentration of the ligand was calculated automatically from the calibration graph obtained for each of the 96-well microplates. The absorbance value was proportional to the content of the analyte in the test sample. Ail the measurements were carried out in duplicate.

Table 10. Protein markers used in the study determining a signature of the presence of lung cancer

# Name Code Manufacturer

IBL International, Hamburg,

1 IBL--EU5931

CRP Germany

CRP high sensitive IBL International, Hamburg,

2 IBL-EU5951

ELISA Germany

NovaTec Immunodiagnostica,

3

CEA NT-DNOV060 Dietzenbach, Germany

NovaTec Immunodiagnostica,

4

CA 125 NT-DNOV061 Dietzenbach, Germany

NovaTec Immunodiagnostica,

5

CA 19-9 NT-DNOV063 Dietzenbach, Germany

Bender MedSystems, Vienna,

6

human t-PA ELISA BS-BMS258/2 Austria ELISA Kit for

Human

Cytokeratin DRG instruments, Marburg, Fragment Antigen Germany

21-1 (CYFRA21-1)

96T DRG-EIA3943

Biomedica Medizinprodukte, Wien,

D K-1 EUSA BI-20412 Austria

ELISA Kit for

Human Squamous

USCN Life Science Inc., Wuhan, Ceil Carcinoma

China

Antigen 1 (SCCA1)

96T USCN-E1372HU

ELISA Kit for

Human Squamous

USCN Life Science Inc., Wuhan, Cell Carcinoma

China

Antigen 2 (SCCA2)

96T USCN-E0159HU

DRG instruments, Marburg,

NSE DRG-EIA2353 Germany

EUSA Kit for

Human Antitrypsin USCN Life Science Inc., Wuhan, Alpha 1 (alAT) China

96T USCN-E1697HU

ELISA Kit for

Human Antitrypsin immunodiagnostik AG, Bensheim, Alpha 1 (alAT) Germany

96T K6752

SAA (Human)

Abnova, Taipei, Taiwan ELISA kit ABN-KA0518

RBP4 (Human)

Abnova, Taipei, Taiwan ELISA kit ABN-KA0499

DiaSorin, Minnesota, USA

Sangtec 100 EUSA IS-364.701

ELISA Kit for

Human Pro

USCN Life Science inc., Wuhan, Gastrin Releasing USCN-E1186HU

China

Peptide (pro-GRP)

96T

ELISA Kit for

Human Tissue

USCN Life Science Inc., Wuhan, Polypeptide

China

Specific Antigen

(TPS) 96T USCN-E1281HU Statistical analysis of data

1. The k-nearest neighbours algorithm (for k=10) was used for the prediction of expression levels for the case of missing data. The missing values were replaced by the median value for the 10 nearest (within the meaning of the Euclidean norm) imiRNAs and proteins (Troyanskaya et al. 2001).

2. The statistical method of logistic regression was used for the construction of the classifier.

3. A preliminary ordering of features from the most to the least significant ones was carried out using the modified Mann- Whitney rank statistic U.

4. The MRV Monte Carlo cross-validation method was used to select the molecular signature. A p of 0.5 was adopted for division of the dataset into the training subset and the testing subset. For each partial model, N=500 of independent draws were performed and based on the results of classification the levels of NPV and PPV were assessed.

5. The final signature is a set of miRNAs and proteins along with the specification of the threshold value thr of the logistic discriminative function of maximising values of NPV at the limitation of PPV>30%.

E) Results

In the present invention, we propose a signature composed of 23 miRNA and 6 proteins which show altered expression in the plasma and serum, respectively. Their list is provided in Table 11. The mean NPV value estimated by MRV in the population for the logistic classifier created on the basis of each individual miRNA from this list is at least 70% (Tables 12 and 13). The use of all the 29 features improves the performance of classification to the NPV level of 96.10% for the traditional method, and to the NPV level of 90.07% for the Monte Carlo multiple validation method. Table 11. List of 23 miRNAs and roteins making up the signature.

UCCCUGUCCUCCAGGAGCUCACG

18 hsa-miR-339-5p

GUGCAUUGUAGUUGCAUUGCA

19 hsa-miR-33a

UGAGGGGCAGAGAGCGAGACUUU

20 hsa-miR-423-5p

AUAUAAUACAACCUGCUAAGUG

21 hsa-miR-374b

AACAUAGAGGAAAU UCCACGU

22 hsa-miR-376c

UCUUCUCUGUUUUGGCCAUGUG

23 hsa-miR-942

Protein

24 DRG-cyfra21-l

Protein

25 IBL-hCRP

Protein

26 DRG-NSE

Protein

27 NT-CEA

Protein

28 NT-CA125

Protein

29 U5CN-SCCA1

Table 12. Estimations (obtained using the traditional method) of the mean values of Sens, SPC, PPV and NPV for the population with respect to the logistic classifier created on the basis of each individual protein or miRNA from the list included in Table 9 along with the individuall selected threshold values thr.

Threshold

miRNA or protein

value thr :¾] en ¾

DRG-NSE 0.254 5.05% 97.96% 45.45% 75.39% sa-miR-122 0.226 71.72% 41.16% 29.10% 81.21%

NT-CEA 0.258 11.11% 97.62% 61.11% 76.53% hsa-miR-33a 0.207 82.83% 24.49% 26.97% 80.90% hsa-let-7f 0.149 100.00% 7.48% 26.68% 100.00% hsa-miR-142-5p 0.202 87.88% 22.11% 27.53% 84.42% hsa-miR-103 0.200 89.90% 25.17% 28.80% 88.10%

NT-CA125 0.257 23.23% 85.37% 34.85% 76.76% hsa-miR-17 0.248 58.59% 52.72% 29.44% 79.08% hsa-miR-148b 0.218 85.86% 27.55% 28.52% 85.26% hsa-miR-107 0.204 94.95% 21.09% 28.83% 92.54% hsa-miR-142-3p 0.169 98.99% 7.14% 26.42% 95.45% hsa-miR-339-5p 0.152 97.98% 6.12% 26.01% 90.00% hsa-miR-29c 0.254 54.55% 57.48% 30.17% 78.97% hsa-miR-374b 0.243 68.69% 49.32% 31.34% 82.39% hsa-miR-199a-3p 0.253 54.55% 56.46% 29.67% 78.67% hsa-miR-27b 0.250 51.52% 53.06% 26.98% 76.47% hsa-miR-376c 0.257 45.45% 64.63% 30.20% 77.87% hsa-miR-340 0.227 84.85% 24.49% 27.45% 82.76%

USCN-SCCA1 0.101 98.99% 12.93% 27.68% 97.44% hsa-miR-942 0.208 92.93% 14.63% 26.82% 86.00% hsa-miR-27a 0.251 58.59% 54.08% 30.05% 79.50% hsa-miR-21 0.254 57.58% 54.08% 29.69% 79.10% hsa-miR-574-3p 0.258 45.45% 63.27% 29.41% 77.50% hsa-miR-181a 0.228 78.79% 35.71% 29.21% 83.33% hsa-miR-144 0.206 87.88% 19.05% 26.77% 82.35% hsa-miR-423-5p 0.260 52.53% 57.82% 29.55% 78.34% Table 13. Estimations (obtained using the Monte Carlo multivariate cross-validation technique [MRV]) of the mean values of Sens, SPC, PPV and NPV for the population with respect to the logistic classifier created on the basis of each individual protein or miRNA from the list included in Table 9 along with the individually selected threshold values thr.

sill Threshold

miRNA or protein

value thr ItfP Vil

1 DRG-cyfra21-l 0.255 27.33% 92.54% 57.20% 79.38%

2 IBL-hCRP 0.180 72.94% 47.36% 31.77% 84.56%

3 DRG-NSE 0.254 41.56% 58.79% 28.08% 71.72%

4 hsa-miR-122 0.226 72.60% 39.15% 28.50% 81.31%

5 NT-CEA 0.258 13.08% 94.51% 44.85% 76.55%

6 hsa-miR-33a 0.207 84.82% 21.49% 26.56% 81.48%

7 hsa-let-7f 0.149 98.61% 7.22% 26.18% 93.43%

8 hsa-miR-142-5p 0.202 88.16% 18.37% 26.55% 82.66%

9 hsa-miR-103 0.200 90.30% 23.43% 28.34% 88.46%

10 NT-CA125 0.257 24.52% 81.38% 29.05% 76.42%

11 hsa-miR-17 0.248 61.16% 48.97% 28.55% 79.27%

12 hsa-miR-148b 0.218 86.68% 23.75% 27.53% 82.03%

13 hsa-miR-107 0.204 91.04% 17.44% 26.95% 84.19%

14 hsa-miR-142-3p 0.169 95.31% 8.19% 25.71% 84.05%

15 hsa-miR-339-5p 0.152 96.17% 5.92% 25.41% 83.39%

16 hsa-miR-29c 0.254 54.73% 55.05% 28.79% 78.54%

17 hsa-miR-374b 0.243 70.52% 43.41% 29.48% 81.44%

18 hsa-miR-199a-3p 0.253 55.80% 49.96% 27.23% 77.15%

19 hsa-miR-27b 0.250 58.02% 47.79% 26.98% 77.14%

20 hsa-miR-376c 0.257 43.98% 63.17% 28.33% 77.21%

21 hsa-miR-340 0.227 85.18% 20.90% 26.40% 79.49%

22 USCN-SCCA1 0.101 98.65% 14.38% 27.77% 97.40% Threshold

ΙΙΙί miRNA or protein

value thr !ifi ci! fiff>§yl

23 hsa-miR-942 0.208 91.53% 14.12% 26.21% 82.21%

24 hsa-miR-27a 0.251 61.49% 46.75% 27.99% 77.81%

25 hsa-miR-21 0.254 57.11% 51.77% 28.15% 78.53%

26 hsa-miR-574-3p 0.258 40.65% 64.87% 27.12% 76.65%

27 hsa-miR-lSla 0.228 80.09% 27.64% 26.98% 78.83%

28 hsa-miR-144 0.206 88.04% 16.61% 26.02% 80.81%

29 hsa-miR-423-5p 0.260 44.29% 61.21% 26.16% 76.88%

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Claims

Patent claims

A predictive method for the detection and/or exclusion of lung cancer which involves the measurement of expression levels of miRNA and the concentrations of proteins in the test sample, involving:

ΓΡΙ Ι¾ or protein miRNA sequence

UUAUAAAGCAAUGAGACUGAUU

hsa-miR-340

UGAGGUAGUAGAUUGUAUAGUU

hsa-let-7f

AGCAGCAUUGUACAGGGCUAUGA

hsa-miR-103

AGCAGCAUUGUACAGGGCUAUCA

hsa-miR-107

UGGAGUGUGACAAUGGUGUUUG

hsa-miR-122

UGUAGUGUUUCCUACUUUAUGGA

hsa-miR-142-3p

CAUAAAG U AG AAAGCACU ACU

hsa-miR-142-5p

UACAGUAUAGAUGAUGUACU

hsa-miR-144

UCAGUGCAUCACAGAACUUUGU

hsa-miR-148b

CAAAGUGCUUACAGUGCAGGUAG

hsa-miR-17

A ACAU U C AACG CUGUCGGUGAGU

hsa-miR-181a ACAGUAGUCUGCACAUUGGUUA hsa-miR-199a-3p

UAGCUUAUCAGACUGAUGUUGA

hsa-miR-21

CACGCUCAUG CAC ACACCCAC A

hsa-miR-574-3p

UUCACAGUGGCUAAGUUCCGC

hsa-mtR-27a

UUCACAGUGGCUAAGUUCUGC

hsa-miR-27b

UAGCACCAUUUGAAAUCGGUUA

hsa-miR-29c

UCCCUGUCCUCCAGGAGCUCACG

hsa-miR-339-5p

GUGCAUUGUAGUUGCAUUGCA

hsa-miR-33a

UGAGGGGCAGAGAGCGAGACUUU

hsa-miR-423-5p

AUAUAAUACAACCUGCUAAGUG

hsa-miR-374b

AAC AU AG AG G A AAU U CC ACG U

hsa-miR-376c

UCUUCUCUGUUUUGGCCAUGUG

hsa-miR-942

Protein

DRG-cyfra21-l

Protein

IBL-hCRP

Protein

DRG-NSE b) Determination of the expression levels of indicated miRNAs and of the concentrations of indicated proteins in the blood relative to the expression levels of indicated miRNAs, by calculating the difference between the reference miRNA level and the measured value of expression of each of the indicated miRNAs.

2. A method according to Clam 1, characterised in that it involves the measurement of the levels of expression of the following miRNAs and of the concentrations of the following protein markers: IBL-hCRP, USCN-SCCA1, hsa- let-7a, hsa-let-7f, hsa-miR-122, hsa-miR-142-5p, hsa-miR-144, hsa-miR-148b, hsa-miR-21, hsa-miR-23b, hsa-miR-27a, hsa-miR-29c.

3. A method according to Clam 1, characterised in that it involves the measurement of the levels of expression of the following miRNAs and of the concentrations of the following protein markers: USCN-SCCA1, IBL-hCRP, hsa- let-7f, hsa-miR-142-5p, hsa-miR-122.

4. A method according to Claims 1 to 3, characterised in that the biological

samples are collected from peripheral blood.

5. A method according to Claim 1, characterised in that lung cancer is early !ung cancer.

6. A method according to Claims 1 to 6, characterised in that the method is used prior to LDCT.

7. A method according to Claims 1 to 6, characterised in that the method is used following LDCT.

8. The use of the method according to Claims 1 to 8, characterised in that the method is employed for the detection of lung cancer in individuals at high risk of lung cancer.

9. A test kit for the detection of lung cancer using the a combination method combining measurement of miRNA expression by PCR with measurement of blood concentrations of proteins by ELISA, characterised in that: — for the miRNAs, the kit contains (i) a reference level determined on the basis of a control kit, and (ii) a biological sample for the measurement of miRNA expression levels using PCR;

— the markers defined in Claim 1 are measured.

10. A kit according to Claim 10, characterised in that it involves the measurement of concentrations of the following protein markers and the measurement of expression levels of the following miRNAs: !BL-hCRP, USCN-SCCA1, hsa-let-7a, hsa-let-7f, hsa-miR-122, hsa-miR-142-5p, hsa-miR-144, hsa-miR-148b, hsa-miR- 21, hsa-miR-23b, hsa-miR-27a, hsa-miR-29c

11. A kit according to Claim 10, characterised in that it involves the measurement of concentrations of the following protein markers and the measurement of expression levels of the following miRNAs: USCN-SCCA1, IBL-hCRP, hsa-let-7f, hsa-miR-142-5p, hsa-miR-122.