HK1136873B - Use of nnmt as a marker for lung cancer - Google Patents

Description

Application of NNMT (N-NNMT) as lung cancer marker

The present invention relates to an adjunct method for the assessment of lung-associated cancer or lung cancer (═ LC) and in particular the assessment of non-small cell lung cancer (NSCLC). Disclosed herein is the use of the protein nicotinamide N-methyltransferase (═ NNMT) as a marker of LC, particularly NSCLC. Furthermore, the present document particularly relates to a method for assessing lung cancer by detecting NNMT in a liquid sample derived from a patient. The detection of NNMT can be used, for example, for the early detection of lung cancer or for the monitoring of patients undergoing surgery.

Despite advances in detection and treatment, cancer remains a major public health challenge. Among the many types of cancer, LC is a common cancer occurring in the western world and is one of the most common causes of cancer-related mortality. This is due in large part to diagnostic gaps for early detection of disease (diagnostic gap). LC is largely asymptomatic in its early stages. Most of all lung cancers are detected at an advanced stage when the disease has become inoperable.

Most LC tumors can be classified as Small Cell Lung Cancer (SCLC) and non-small cell lung cancer (NSCLC). SCLC accounts for about 20-25% of all lung cancer cases. SCLC is an aggressive neuroendocrine type of LC and has a poor prognosis even if detected early. SCLC is a rare disease susceptible to radical treatment by resection. Due to the rapid progression of the disease, SCLC is usually classified with only two stages, limited disease and extended disease, rather than the more complex TNM staging system (see below). Approximately 75-80% of cases of LC are classified in the NSCLC category, which includes squamous cell carcinoma (carcinoma ═ CA), adeno-CA (a subset comprising acinar CA, papillary CA, bronchial tumors, solid tumors, and mixed subtypes), and large cell carcinoma (a subset comprising large cell tumors, clear cell CA, adenosquamous CA, and undifferentiated CA).

The prognosis is also poor if NSCLC is detected at an advanced stage. The stage of cancer is a classification of the disease by spread, aggressiveness and severity. Cancer patients were grouped to generalize prognosis and choice of therapy.

Today, the TNM system is the most widely used classification system based on the anatomical category of cancer. Which represents an internationally accepted, unified staging system. There are three basic variations: t (extent of primary tumor), N (status of regional lymph nodes), and M (presence or absence of distant metastasis). The TNM standard is defined by UICC (International Union anticancer cancer) at: the TNM Classification of malignant tumors (TNM Classification of MalignantTumours), fifth edition, Sobin, L.H., and Wittekind, Ch. (eds.), Wiley-Liss (1997), pp. 1803-1804).

Surgical resection of primary tumors is a widely accepted treatment option for early stage NSCLC. With the progression of NSCLC, particularly the transition from stage IIIa (T3N1M0, T1N2M0, T2N2M0, T3N2M0) to stage IIIb (T4N0M0, T4N1M0, T4N2M0), the physician's important emergency approaches have declined significantly. However, if the cancer is detected at an earlier stage (Ia-IIIa; preferably earliest to stage T3N1M0), the five-year survival rate varies between 35% and 80%. Detection at stage Ia ((T1N0M 0); small tumor size, no metastasis) clearly has the best prognosis with a five-year survival rate of up to 80%.

Surgical intervention in stage IIIb-IV of NSCLC is rarely, if ever, used. Stage IV corresponds to distant metastasis, i.e. spread of the disease away from the lungs and regional lymph nodes. The five-year survival rate of the later stages (III-IV) decreased to between less than 1% and 15%.

Of particular importance, early diagnosis of NSCLC shifts the prognosis better. Patients diagnosed as early as in stages Ia (T1N0M0), Ib (T2N0M0), IIa (T1N1M0), IIb, (T3N0M0) and IIIa (T3N1M0) have up to 80% chance of survival 5 years after diagnosis if treated properly. For the patient to be diagnosed, once distant metastasis has occurred, a comparison has to be made with a 5-year survival rate of less than 1%.

In the present context, early assessment of LC refers to assessment at the T1N0M0 or T1-3N0-1M0 stage of the tumor.

Preferably, LC is evaluated during the period T1-3N0-1M0(═ T1-3N0-1M 0).

Most lung cancers are detected when they become symptomatic. Current detection methods include chest X-ray fluoroscopy, spiral computer tomography, sputum cytology and bronchoscopy. However, there is debate as to the suitability of these methods for population screening.

Many serum tumor markers for lung cancer are in clinical use. Soluble 30kDa fragments of cytokeratin 19(CYFRA 21-1), carcinoembryogenic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) are the most prominent LC markers. However, none of them meets the criteria of sensitivity and specificity required by screening tools (Thomas, L., Labor und Diagnose, TH BooksVerlagsgesellschaft, Frankfurt/Main, Germany (2000)).

To be clinically useful, a novel diagnostic marker as a single marker should be comparable or better than other markers known in the art. Alternatively, the new marker should result in an improved sensitivity and/or specificity of diagnosis, respectively, even if used alone or in combination with one or more other markers. The diagnostic sensitivity and/or specificity of the assay is best assessed by its subject performance characteristics as will be described in detail below.

Whole blood, serum or plasma are the most widely used sample sources in clinical routine. The identification of early LC tumor markers, which should help in reliable cancer detection or provide early prognostic information, can lead to methods that should help in the diagnosis and treatment of the disease. Therefore, there is an urgent clinical need to improve the in vitro assessment of LC. It is particularly important to improve the early diagnosis of LC, since for patients diagnosed early on, the need for survival opportunities is higher than for those diagnosed at the progressive stage of the disease.

The clinical use of biochemical markers in lung cancer has recently been reviewed (Duffy, M.J., Crit. Rev. Clin. Lab. Sci.38(2001) 225-262).

CYFRA21-1 is currently considered to be the best known tumor marker for lung cancer. Although not organ-specific, it is found primarily in lung tissue. The sensitivity of CYFRA21-1 to lung cancer is described as 46-61% at 95% specificity for other benign lung diseases. Elevated serum levels of CYFRA21-1 are also associated with significant benign liver disease, renal insufficiency and invasive bladder cancer. The CYFRA21-1 test is recommended for postoperative treatment monitoring.

CEA belongs to the group of carcinoembryonic antigens that are normally produced during embryogenesis. CEA is not organ specific and is used primarily for monitoring of colorectal cancer. In addition to malignancies, several benign diseases such as cirrhosis, bronchitis, pancreatitis and autoimmune diseases are also associated with elevated CEA serum levels. At 95% specificity for benign lung disease, its sensitivity for lung cancer is reported to be 29-44%. A preferred use for CEA is therapy monitoring of lung cancer.

NSE is a tumor marker for SCLC. Generally, elevated NSE serum levels are found in association with neuroectodermal and neuroendocrine tumors. Elevated serum levels are also found in patients with benign lung diseases and brain diseases, such as meningitis or other inflammatory diseases of the brain, as well as head trauma. When the sensitivity to SCLC is 95%, the reported specificity is 60-87%, and for NSCLC, the NSE test performs poorly (7-25%). NSE is recommended for therapy monitoring of SCLC.

SCC was initially identified in cervical squamous cell CA. The sensitivity of SCC is typically low for LC (18-27%). Therefore, SCC testing is considered inappropriate for screening. However, due to the relatively high sensitivity to squamous cell CA, the preferred application of SCC is therapy monitoring, although CYFRA21-1 generally performs better.

As for the distribution of markers and for improved diagnosis of lung cancer, a method was disclosed (Schneider, J. et al, int. J. Clin. Oncol.7(2002)145-151) which combines serum levels of CYFRA21-1, NSE and C-reactive protein (CRP), a general inflammatory marker, using a classification algorithm based on fuzzy logic. The authors report a sensitivity of 92% at a specificity of 95%.

The task of the present invention is to investigate whether biochemical markers can be identified, which can be used to evaluate LC.

Surprisingly, it has been found that the use of the marker NNMT can at least partially overcome some of the problems of the markers known in the prior art.

The present invention relates to a method for the in vitro assessment of lung cancer, the method comprising detecting the concentration of NNMT in a sample and using the determined concentration in the assessment of lung cancer. Surprisingly, it was determined that elevated levels of NNMT in a sample, for example, are indicative of the presence of lung cancer in a patient.

The invention also relates to a method for the in vitro assessment of LC by biochemical markers, which method comprises detecting the concentration of NNMT and one or more other markers of LC in a sample and using the determined concentration in the assessment of LC. Preferably, the one or more other marker of LC is selected from CYFRA21-1, CEA, NSE and SCC.

In a preferred embodiment, the invention also relates to the use of a marker panel (marker panel) comprising at least NNMT and CYFRA21-1 in the assessment of LC.

The invention also relates to the use of a marker panel comprising at least NNMT and CEA in the assessment of LC.

The invention also relates to the use of a marker panel comprising at least NNMT and SCC in the assessment of LC.

In a preferred embodiment, the present invention relates to a method for the in vitro assessment of lung cancer, the method comprising detecting in a sample a) the concentration of NNMT, b) optionally the concentration of one or more other lung cancer markers, and c) using the concentrations determined in step (a) and optionally step (b) in the assessment of lung cancer.

NNMT (nicotinamide N-methyltransferase, EC 2.1.1.1) catalyzes the N-methylation of nicotinamide and other pyridines. The protein NNMT (Swiss-PROT: P40261) is characterized by the sequence listing given as SEQ ID NO: 1. NNMT has an apparent molecular weight of 29.6kDa and an isoelectric point of 5.56.

NNMT activity is important for the biotransformation of many drugs and xenobiotic compounds. It has been reported that this protein is mainly expressed in the liver and localized in the cytoplasm. NNMT was cloned from cDNA of human liver and contains 792-nucleotide open reading frame encoding 264-amino acid protein, which has a calculated molecular weight of 29.6kDa (Aksoy, S. et al, J.biol.chem.269(1994) 14835-14840). Little is known in the literature about the potential role of this enzyme in human cancer. In one paper, elevated hepatic NNMT enzyme activity has been reported as a marker of cancer cachexia in mice (Okamura, A. et al, Jpn.J.cancer Res.89(1998) 649-656). In recent reports, down-regulation of NNMT genes in response to radiation has been demonstrated in radiation-sensitive cell lines (Kassem, H., et al, Iht. J. cancer 101(2002) 454-460). In US 2006/0024692, NNMT m-RNA-levels were found to be lower in non-small cell lung cancer cells than in non-cancer tissues. WO 02/082076 describes the NNMT protein as a marker for subtypes of kidney cancer.

As used herein, each of the following terms has the meaning associated with its section below.

The articles "a" and "an" are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. For example, "label" refers to one label or more than one label.

As used herein, the term "marker" or "biochemical marker" refers to a molecule that is to be used as a target in the analysis of a patient sample. Examples of such molecular targets are the protein or polypeptide itself as well as antibodies directed against such targets as present in the sample. The expression "one or more" means 1 to 20, preferably 1 to 10, preferably l to 5, more preferably 3 or 4.

Proteins or polypeptides for use as markers in the present invention are intended to include naturally occurring variants of said proteins as well as naturally occurring fragments or complexes of said proteins or of said variants, in particular immunologically detectable fragments or complexes, respectively.

As will be clear to the skilled person, the present invention should not be construed as being limited to SEQ ID NO: 1, detection of the full-length protein NNMT. Physiological fragments of NNMT can also be detected and used as markers for lung cancer in the practice of the present invention. The immunodetectable fragment preferably comprises at least 6, 7, 8, 10, 12, 15 or 20 contiguous amino acids of said marker polypeptide. One skilled in the art will recognize that proteins released by cells or present in the cell matrix may be destroyed, for example during inflammation, and may become degraded or cleaved into such fragments. As will be appreciated by those skilled in the art, NNMT or a fragment thereof may also be present as part of a complex. Such complexes may also be used as labels in the sense of the present invention. Additionally, or alternatively, the NNMT polypeptide may carry post-translational modifications, preferably glycosylation, acylation and/or phosphorylation, and such modified NNMT may also serve as a marker of LC.

The term "assessing lung cancer" is used to indicate that the method according to the invention will (alone or together with other markers or variables, such as criteria established by the UICC (see above)) e.g. assist a physician in establishing or determining the absence or presence of LC or in prognosis, detect rehabilitation (patient follow-up after surgery) and/or therapy monitoring, especially chemotherapy monitoring.

The term "sample" as used herein refers to a biological sample obtained for the purpose of in vitro evaluation. In the method of the invention, the sample or patient sample may preferably comprise any body fluid. Preferably the sample is whole blood, serum, plasma, bronchial lavage or sputum, respectively, with plasma or serum being most preferred.

Surprisingly, the inventors of the present invention have been able to detect the marker NNMT in a significant percentage of samples derived from patients with LC. Even more surprisingly, they have been able to demonstrate that the presence of NNMT in such a liquid sample obtained from a patient can be used in the assessment of lung cancer.

In a preferred embodiment, the invention relates to a method for assessing LC by measuring the concentration of NNMT in a sample and using the measured concentration to assess LC.

As the skilled artisan will recognize, any such detection is performed in vitro. Patient samples were discarded after. Patient samples are used solely for the in vitro diagnostic method of the invention and the material of the patient sample is not transferred into the patient's body. Characteristically, the sample is a liquid sample, such as whole blood, serum or plasma.

As the skilled person will recognise from the described examples, by detecting in an immunoassay that NNMT has been identified as a marker for the diagnosis of LC, results comparable to the achievements of the invention can be achieved using alternative approaches. The marker protein NNMT may be detected by any suitable method and used as a marker for LC. Such preferred suitable methods include detection of NNMT by an immunoassay procedure, by any other form of binding assay, by detection of the enzymatic activity thereof, by liquid chromatography, especially high performance liquid chromatography, by electrophoresis, especially SDS-PAGE combined with Western blotting and by mass spectrometry.

An ideal protocol for diagnosis would be a situation where a single event or process would cause the respective disease, as in infectious diseases. In all other cases, correct diagnosis can be very difficult, especially when the etiology of the disease is not fully understood as a case of LC. As the skilled person realizes, no biochemical markers, e.g. in the field of LC, are diagnosed 100% specifically and 100% sensitive for a given disease. More suitably, biochemical markers such as CYFRA21-1, CEA, NSE, SCC or NNMT as shown herein can be used to assess the presence or absence of disease with certain possible and predictive values. Thus, in routine clinical diagnosis, often a variety of clinical symptoms and biomarkers are considered together in the diagnosis, treatment and management of the underlying disease.

The biochemical markers can be either individually measured or, in a preferred embodiment of the invention, they can be detected simultaneously using a chip or magnetic beads based array technology. The concentration of the biomarkers is then either interpreted independently using individual cut-off values for each marker or they are combined for interpretation.

In a further preferred embodiment, the assessment of LC according to the invention is performed in a method comprising detecting in a sample a) the concentration of NNMT, b) the concentration of one or more other lung cancer markers and c) using the concentrations determined in step (a) and step (b) in the assessment of lung cancer. As will be apparent to the skilled artisan, the test for NNMT and the test for one or more other lung cancer markers can alternatively be performed from an aliquot of the sample or from a different aliquot of the sample.

According to the data presented in the examples section, the marker NNMT, both in the single-factor analysis performed and in the multi-factor analysis, had a (specificity of about 95%) significant sensitivity of almost 50% for LC, and was found in this respect to be comparable or even higher than the other LC markers studied in comparison. In the assessment of LC, the marker NNMT will be advantageous in one or more of the following: screening; diagnosis assistance; prognosis; therapy monitoring, such as monitoring of chemotherapy, radiotherapy and immunotherapy.

Screening:

screening is defined as the systematic application of tests in persons who have not been hospitalized for symptoms of LC to determine individuals at sufficient risk of developing LC, with benefits from further research or direct preventive action.

As the data presented in the examples section demonstrate, NNMT alone will not be sufficient for general screening of, for example, at risk populations of LC. Most likely, no single biochemical marker in the circulation will at any time meet the sensitivity and specificity criteria required for screening purposes. Rather it has to be expected that a marker plate will have to be used in LC screening. The data determined in the present invention indicate that the marker NNMT will form an integral part of a marker panel suitable for screening purposes. The present invention therefore relates to the use of NNMT as one marker of a LC marker panel for LC screening purposes. An elevated level of NNMT indicates the presence of LC. The data herein also indicate that certain combinations of markers would be advantageous in the screening of LC. Thus, the present invention also relates to the use of a marker panel comprising NNMT and CYFRA21-1, or a marker panel comprising NNMT and CEA, or a marker panel comprising NNMT and NSE, or a marker panel comprising NNMT and SCC, or a marker panel comprising NNMT and two markers selected from the group consisting of CYFRA21-1, CEA, NSE and SCC, for LC screening purposes.

Diagnosis assistance:

the markers may either aid in the differential diagnosis of benign versus malignant disease of a particular organ, or help to distinguish between different tissue types of a tumor. CEA, CA 125, CYFRA21-1, SSC and NSE were used as serum markers to aid in the histological diagnosis of LC as reported by Molina, R.et al, Tumor biol.24(2003) 209-. The results of this study also indicate that among the tested markers CYFRA21-1 is the most sensitive marker in lung cancer, but not histologically.

In accordance with the present data, since NNMT as a single marker may be superior to other LC markers such as CEA or NSE, it must be expected that NNMT will be used as a diagnostic aid, particularly in establishing a baseline value prior to surgery. Thus, the present invention also relates to the use of NNMT to establish a baseline value for LC pre-surgery.

Prognosis:

prognostic indicators can be defined as clinical, pathological, or biochemical features of cancer patients and their tumors that predict disease outcome. Their main application is to help rationally plan patient intervention, i.e. to avoid under-treatment for progressive disease and over-treatment for disease where inertia is no longer progressing. Molina r. et al, Tumor Biol. (2003) 24: 209-218 evaluated the prognostic value of CEA, CA 125, CYFRA21-1, SSC and NSE in NSCLC. If the overall survival of the patient is evaluated, abnormal serum levels of the markers NSE, CEA and LDH (lactate dehydrogenase) appear to indicate a shorter survival period.

Since NNMT alone has contributed significantly to the differentiation of LC patients from healthy controls, it must be expected that it will play an auxiliary role in the evaluation of the prognosis of patients suffering from LC. The preoperative NNMT levels are most likely used in combination with one or more other markers of LC and/or the TNM staging system. In a preferred embodiment, NNMT is used for the prognosis of LC patients.

Monitoring chemotherapy:

merle, P.et al, int.J.of Biological Markers 19(2004)310-315 has evaluated CYFRA21-1 serum level changes in patients with locally advanced NSCLC treated with induced chemotherapy. They concluded that early monitoring of CYFRA21-1 serum levels could be a useful predictive tool for tumor response and survival in stage III NSCLC patients. In addition, reports describe the use of CEA in the monitoring of LC patient treatment (Fukasawa, T. et al, Gan to Kagaku Ryoho. cancer & Chemotherpy 13 (1986)) 1862-1867 (written in Japanese); Zhang, H. et al, Shandong Yike Daxue Xuebao 39(2001)537-538, 541 (written in Chinese)). Most of these are retrospective, non-randomized and include a small number of patients. As in the case of the study with CYFRA21-1, the CEA study suggests: a) patients with reduced CEA levels often have a better outcome when receiving chemotherapy than those patients who fail to have reduced CEA levels. And (b) for almost all patients, elevated CEA levels are associated with disease progression.

Due to the data shown in the examples section, it must be expected that NNMT should be at least as good a marker for chemotherapy monitoring as CYFRA21-1 or CEA. The invention therefore also relates to the use of NNMT for monitoring LC patients undergoing chemotherapy.

Follow-up:

the goal of most LC patients undergoing surgical resection is complete removal of cancerous tissue, late-stage progression of recurrent or metastatic disease (Wagner, H., Chest 117(2000) 110-. Most of these relapses occur within the first 2-3 years after surgery. Since recurrent/metastatic disease is always fatal, considerable research has focused on the identification of LC at an early and thus potentially treatable stage.

Thus, many LC patients undergo a post-operative monitoring procedure, which often includes routine monitoring with CEA. Continuous monitoring with CEA after surgical resection for one year has shown that early postoperative recurrent/metastatic disease is detected with a sensitivity of about 29% with a specificity of about 97% even in the absence of suspected symptoms or signs (Bucchei, G., et al, Ann. Thorac. Surg.75(2003) 973-. Thus, post-operative follow-up of LC patients is one of the most important areas in the application of suitable biochemical markers. Due to the high sensitivity of NNMT in the LC patients studied, it is expected that NNMT alone or in combination with one or more other markers will be of great help in the follow-up of LC patients, especially in LC patients after surgery. The use of a marker panel comprising NNMT and one or more other markers of LC in the follow-up of LC patients represents a more preferred embodiment of the invention.

In a preferred embodiment the invention relates to the use of NNMT in the diagnostic field of LC or in the assessment of LC, respectively.

In a still more preferred embodiment, the present invention relates to the use of NNMT as a marker molecule for lung cancer in combination with one or more marker molecules for lung cancer in the assessment of lung cancer from a liquid sample obtained from a patient.

Thus, a preferred embodiment of the invention is the use of NNMT as a marker molecule for lung cancer in combination with one or more marker molecules for lung cancer in the assessment of lung cancer from a liquid sample obtained from a patient. Preferred other LC markers selected that can be combined with the detection of NNMT are CYFRA21-1, CEA, NSE and SCC. Still more preferably, the marker panel used for the assessment of LC comprises NNMT and at least one further marker molecule selected from the group consisting of CYFRA21-1 and CEA.

As will be appreciated by those skilled in the art, there are many ways to apply the detection of two or more markers to improve the diagnostic problem under investigation. In a very simple, but nevertheless often effective means, a positive result is assumed if the sample is positive for at least one marker under investigation. This may be the case, for example, when diagnosing infectious diseases such as AIDS.

Often, however, combinations of markers are evaluated. Preferably, the detection values for the markers of the marker panel, e.g., for CYFRA and CEA, are mathematically combined and the combined value is correlated with a potential diagnostic problem. The marker values can be combined by any suitable provision in the mathematical methods of the art. Familiar mathematical methods for correlating marker combinations with disease use methods such as Discriminant Analysis (DA) (i.e., linear-, quadratic-, modified-DA), Kernel methods (i.e., SVM), nonparametric methods (i.e., k-Nearest-Neighbor Classifiers), PLS (Partial least squares), tree-Based (tree-Based) methods (i.e., logistic Regression), CART, random Forest methods, Boosting/Bagging methods), generalized Linear Models (i.e., Logistic regression), Principal component based (i.e., Principal Components based) methods (i.e., SIMCA), Generalized Additive Models (Generalized Additive Models), Fuzzy Logic based (Fuzzy Logic based) methods, Neural network and genetic algorithm based (Neural Networks and genetic algorithms) methods. The skilled person will have no problem in selecting a suitable method for evaluating the marker combination of the invention. Preferably, the method used to correlate the marker combinations of the invention, e.g. with the absence or presence of LC, is selected from DA (i.e. linear-, quadratic-, regularized discriminant analysis), Kernel methods (i.e. SVM), nonparametric methods (i.e. k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e. logistic regression, CART, random Forest methods, Boosting methods) or generalized linear models (i.e. logistic regression). Details on these statistical methods are found in the following references: ruczinski, I.E., J.of Computational and graphical Statistics 12(2003) 475-; friedman, J.H., journal of the American Statistical Association 84(1989) 165-; hastie, T, et al, principles of Statistical knowledge (The Elements of Statistical Learning), Springer Statistical Series plexuses (Springer Series in Statistics) (2001); breiman, L.et al, Classification and Regression Trees (Classification and Regression Trees), Wadsworth International group, Belmeng (Belmont), Calif. (1984); breiman, L., Random forms, mechanical Learning (Machine Learning), 45(2001) 5-32; pepe, m.s., Statistical evaluation of Medical trials for Classification and Prediction (The Statistical evaluation of Medical Tests for Classification and Prediction), Oxford Statistical Science Series (Oxford Statistical Science Series), 28 (2003); and duca, r.o. et al, Pattern Classification (Pattern Classification), Wiley Interscience, 2 nd edition (2001).

A preferred embodiment of the invention is to use optimized multivariate cut-off values for potential biomarker combinations and to distinguish between state a and state B, e.g. to distinguish between disease and health. In this type of assay, the label is no longer present independently and forms a label plate. It was determined that combining the detection of NNMT with the detection of CYFRA21-1 or CEA, respectively, significantly improves the accuracy of the diagnosis of LC compared to healthy controls.

The accuracy of the diagnostic method is best described by its Receiver Operating Characteristics (ROC) (see in particular Zweig, M.H. and Campbell, G., Clin. chem.39(1993) 561-. The ROC plot is a plot of all sensitivity/specificity pairs resulting from varying their decision threshold continuously over the entire range of observed data.

The clinical performance of a laboratory test depends on the accuracy of its diagnosis or the ability to accurately classify patients into clinically relevant subgroups. Diagnostic accuracy the ability of a test to accurately distinguish between two different conditions in a patient under study is examined. Such situations are e.g. health and disease or benign versus malignant disease.

In each case, the ROC plot depicts the overlap between the two distributions by plotting sensitivity versus 1-specificity for the full range of decision thresholds. On the y-axis is the sensitivity or true positive fraction [ defined as (number of true positive test results)/(number of true positives + number of false negative test results) ]. This is also referred to as positive in the presence of a disease or disorder. Calculated separately from the affected subgroups. On the x-axis is the false positive fraction or 1-specificity [ defined as (number of false positive results)/(number of true negatives + number of false positive results) ]. It is an index of specificity and is calculated entirely from the unaffected subgroup. Because the true positive and false positive fractions are calculated completely separately, the ROC plot is independent of the incidence of disease in the sample by using test results from two different subgroups. Each point on the ROC plot represents a sensitivity/1-specificity pair corresponding to a particular decision threshold. The test with perfect discrimination (no overlap in the distribution of the two results) has ROC plots throughout the upper left corner where the true positive fraction is 1.0 or 100% (accurate sensitivity) and the false positive fraction is 0 (accurate specificity). The theoretical plot for the experiment without distinction (both groups have the same distribution of results) is a 45 ° diagonal line from the lower left corner to the upper right corner. Most of the figures fall between these two poles. (if the ROC plot falls well below the 45 ° slope, it is easy to correct, by reversing the criteria for "positive", from "greater" to "less" or vice versa). Qualitatively, the closer the plot is to the upper left corner, the higher the overall accuracy of the experiment.

One convenient goal of quantifying the diagnostic accuracy of a laboratory test is to express its performance in a single number. Such a complete parameter is, for example, the so-called "total error" or alternatively "area under the curve ═ AUC". The most common overall measure is the area under the ROC plot. Conventionally, the area is always ≧ 0.5 (if not, anyone can reverse this decision rule to make it conform). The range of values is between 1.0 (test values of both groups are completely separated) and 0.5 (no significant difference in distribution between the two test values). The area depends not only on the specific part of the plot, such as the point closest to the slash or the sensitivity at 90% specificity, but on the overall plot. This is a quantitative, descriptive expression, i.e. the ROC plot is the one that is closest to perfect (area 1.0).

Combining the detection of NNMT with the detection of other markers, such as CYFRA21-1 or CEA, or with the detection of other markers of LC yet to be discovered, NNMT leads to and will lead to a further improvement in the assessment of LC.

The combination of the two markers NNMT and CYFRA21-1 significantly improved the accuracy of the diagnosis of LC. The combination of the two markers NNMT and CEA also significantly improves the accuracy of the diagnosis of LC.

In a preferred embodiment, the present invention relates to a method for improving the diagnostic accuracy of LC relative to healthy controls by measuring in a sample the concentration of at least NNMT and CYFRA21-1, CEA, NSE or SCC, respectively, and correlating the concentrations determined with the presence or absence of LC, which improvement results in more patients being correctly classified as suffering from LC versus healthy controls compared to a classification based on any single marker studied alone.

In a preferred method according to the invention, the concentration of at least the biomarkers NNMT and CYFRA21-1, respectively, is determined and the marker combination is used for the assessment of LC.

In a more preferred method according to the invention, the concentration of at least the biomarkers NNMT and CEA, respectively, is determined and the marker combination is used for the assessment of LC.

In yet a more preferred method according to the invention, the concentration of at least the biomarkers NNMT, CYFRA21-1 and CEA, respectively, is determined and the marker combination is used for the assessment of LC.

In yet a more preferred method according to the invention, the concentration of at least the biomarkers NNMT, CYFRA21-1 and SCC, respectively, is determined, and the marker combination is used for the assessment of LC.

The following examples are provided to aid the understanding of the present invention, the true scope of which is set forth in the appended claims. It is to be understood that modifications can be made to the listed methods without departing from the spirit of the invention.

Example 1

Production of antibodies to the Lung cancer marker protein NNMT

To further use the antibodies in the detection of serum and plasma and blood levels of NNMT by immunodetection assays such as western blot and ELISA, polyclonal antibodies to the lung cancer marker protein NNMT were generated.

Expression of recombinant proteins in E.coli (E.coli)

To generate antibodies to NNMT, recombinant expression of the protein is performed to obtain the immunogen. Coli using a combination of RTS100 expression system and expression in e. In the first step, the DNA sequence was analyzed using the "proteexpert RTS e.coli HY" system and the recommended high yield of cDNA silent mutation variables and the respective PCR-primer sequences were obtained. This is a network-based business service (www.proteoexpert.com). A Linear PCR Template was generated from cDNA and for in vitro transcription and expression of nucleosides using the recommended primer pair "RTS 100 e. coli Linear Template Generation Set (His-tag)" (Roche Diagnostics GmbH, mannheim, germany, catalogue No. 3186237) system and sequences encoding NNMT proteins were used. For Western blot (Western blot) detection and subsequent purification, the expressed protein contained a His-tag (His-tag). The best expressing variables are determined. All steps from PCR to expression and probing were performed according to the manufacturer's instructions. The respective PCR products (promoter, ribosome binding site and T7 terminator) containing all the necessary T7 regulatory regions were cloned into pBAD according to the manufacturer's instructionsVector (Invitrogen, Carlsrue, Germany, Cat. No. K4300/01). For expression, the construct was converted to e.coli BL 21(DE 3) (Studier, f.w., et al, Methods enzymol.185(1990)60-89 using the T7 regulatory sequence and the transformed bacteria were cultured in 11 batches for protein expression.

Purification on a nickel-chelate column according to standard proceduresHis-NNMT fusion protein. Briefly, culture broth of 11 bacteria containing the vector for expression of His-NNMT fusion protein was pelleted by centrifugation. The cell pellet was resuspended in cell lysis buffer containing phosphate (pH8.0), 7M guanidinium chloride, imidazole and thioglycerol, followed by Ultra-And (6) homogenizing. Insoluble material was precipitated by high speed centrifugation and the supernatant was applied to a nickel-chelate chromatography column. The column was washed with several bed volumes of cell lysis buffer followed by a buffer containing phosphate (pH8.0) and urea. Finally, bound antigen was eluted under acidic conditions with a phosphate buffer containing SDS.

Generation of polyclonal antibodies

a) Immunization

For immunization, fresh emulsions of protein solution (100. mu.g/ml protein NNMT) and complete Freund's adjuvant (complete Freund's adjuvant) were prepared in a ratio of 1: 1. Each rabbit was immunized with 1ml of emulsion on days 1, 7, 14 and 30, 60 and 90. Blood was drawn and the resulting anti-NNMT serum was used in the next experiment described in examples 3 and 4.

b) IgG (immunoglobulin G) was purified from rabbit serum by sequential precipitation with hexanoic acid and ammonium sulfate

1 volume of rabbit serum was diluted with 4 volumes of acetate buffer (60mM, pH 4.0). The pH was adjusted to 4.5 with 2M Tris-base. Octanoic acid (25. mu.l/ml of diluted sample) was added dropwise with vigorous stirring. After 30min the sample was centrifuged (13000Xg, 30min, 4 ℃), the precipitate discarded and the supernatant collected. The pH of the supernatant was adjusted to 7.5 by adding 2M Tris-base and filtered (0.2 μ M).

The immunoglobulin in the supernatant was precipitated by dropwise addition of a 4M ammonium sulfate solution with vigorous stirring to a final concentration of 2M. The precipitated immunoglobulins were collected by centrifugation (8000Xg, 15min, 4 ℃).

Is discarded onAnd (5) clear liquid. The precipitate was dissolved in 10mM NaH₂PO₄NaOH, pH7.5, 30mM NaCl and thorough dialysis. The dialysate was centrifuged (13000Xg, 15min, 4 ℃) and filtered (0.2 μm).

Biotinylation of polyclonal rabbit IgG

Polyclonal rabbit IgG was dissolved in 10mM NaH₂PO₄NaOH, pH7.5, 30mM NaCl to 10 mg/ml. To each ml of IgG solution was added 50. mu.l of biotin-N-hydroxysuccinimide (3.6mg/ml in DMSO). After 30min at room temperature, the samples were chromatographed on Superdex 200 (10mM NaH)₂PO₄NaOH, pH7.5, 30mM NaCl). The fraction containing biotinylated IgG was collected. Monoclonal antibodies were biotinylated according to the same protocol.

Digoxigenylation of polyclonal Rabbit IgG (digoxigenylation)

Polyclonal rabbit IgG was dissolved in 10mM NaH₂PO₄NaOH, 30mM NaCl, pH7.5 to 10 mg/ml. To each ml of IgG solution was added 50. mu.l digoxigenin-3-O-methylcarbonyl-epsilon-aminocaproic acid-N-hydroxysuccinimide ester (Roche Diagnostics, Mannheim, Germany, cat # 1333054) (3.8mg/ml in DMSO). After 30min at room temperature, the sample was washed with waterChromatography on 200 (10mM NaH)₂PO₄NaOH, pH7.5, 30mM NaCl). Fractions containing digoxigenin-coupled IgG were collected. The monoclonal antibody was labeled with digoxigenin according to the same protocol.

Example 2

ELISA for the detection of NNMT in human serum and plasma samples.

To detect NNMT in human serum or plasma, a sandwich ELISA was developed. To capture and detect the antigen, aliquots of the anti-NNMT polyclonal antibody (see example 2) were conjugated with biotin and digoxigenin, respectively.

Streptavidin-coated 96-well microtiter plates were incubated with 100. mu.l biotinylated anti-NNMT polyclonal antibody at 10. mu.g/ml in 10mM phosphate, pH7.4, 1% BSA, 0.9% NaCl and 0.1% Tween 20 for 60 min. After incubation, plates were washed three times with 0.9% NaCl, 0.1% tween 20. Wells were then incubated with either serially diluted recombinant protein as a standard antigen (see example 2) or modified plasma samples from patients for 2 h. After binding NNMT, plates were washed three times with 0.9% NaCl, 0.1% tween 20. To specifically detect bound NNMT, wells were incubated with 100. mu.l of digoxigenin-conjugated anti-NNMT polyclonal antibody at 10. mu.g/ml in 10mM phosphate, pH7.4, 1% BSA, 0.9% NaCl and 0.1% Tween 20 for 60 min. Thereafter, the plate was washed three times to remove unbound antibody. In the next step, the wells were incubated with 20mU/ml of anti-digoxigenin-POD conjugate (Roche diagnostics GmbH, Mannheim, Germany, catalog No. 1633716) in 10mM phosphate, pH7.4, 1% BSA, 0.9% NaCl and 0.1% Tween 20 for 60 min. The plates were then washed three times with the same buffer. To detect antigen-antibody complexes, wells were incubated with 100 μ l ABTS solution (Roche Diagnostics GmbH, mannheim, germany, cat # 11685767) and OD was detected after 30-60min at 405nm with an ELISA plate reader.

Example 3

Study I: research population

In the first study, samples derived from 56 well-characterized LC patients according to the UICC classification shown in table 1 were used. This group of patients focuses on NSCLC (50 samples) because LC of this type has a better prognosis than SC type and thus its early detection is particularly important.

TABLE 1

Study I: LC sample and corresponding UICC classification

According to the stage of UICC	Number of samples
		UICCI	7
UICCII	17
		UICCIII	14
UICCIV	14
		Without staging	4
Total number of LC samples	56
		Wherein the number of NSCLC samples	50
Number of SCLC samples therein	6

The LC samples of table 1 were evaluated in comparison to control samples obtained from 121 apparently healthy individuals without any known malignant lung disease (control group).

Example 4

Study I: sensitivity of Single marker

The sensitivity of each marker was calculated at a common specificity level of 95% for each individual marker to be tested. Table 2 gives the sensitivity of each of the most promising LC markers in percent.

TABLE 2

Study I: sensitivity of Single marker

Marker substance	NNMT	CEA	NSE	CYFRA21-1
					Sensitivity at 95% specificity (%)	50.0	42.9	12.5	69.6

As is readily apparent from table 2, the marker NNMT has been found to have the second highest sensitivity for LC, second only to CYFRA21-1 in all the markers studied. The marker CEA appears to have significantly lower sensitivity, while NSE appears to perform quite poorly.

Example 5

Study I: marker combinations

Classifying the individual as having LC if at least one of the markers of the respective combination exceeds a certain threshold. These cut-off values are defined, for example, as giving 95% specificity over the control group.

Table 3 shows the classification of patients diagnosed with LC versus healthy controls.

TABLE 3

Study I: classification of patients in a cohort diagnosed with LC versus healthy controls

Number of markers	Markers or marking plates	Critical value	Sensitivity (%)
				1	Cyfra 21-1	1.7ng/ml	69.6
1	NNMT	884pg/ml	50.0
				1	CEA	5.5ng/ml	42.9
1	NSE	17.7ng/ml	12.5
				2	Cyfra 21-1，CEA	1.9ng/ml5.8ng/ml	73.0
2	Cyfra 21-1NNMT	1.9ng/ml970pg/ml	77.0
				2	NNMT，CEA	1100pg/ml6ng/ml	66.0
2	NNMTNSE	884pg/ml26ng/ml	50.0
				3	NNMTCyfra 21-1CEA	970pg/ml1.9ng/ml6ng/ml	77.0

The combination of markers NNMT and Cyfra21-1 gave the highest sensitivity at the 95% specificity level in this assay. A variety of other markers were evaluated in combination with NNMT. As shown in table 3, CEA combined with NNMT also resulted in a significant improvement in sensitivity.

Example 6

Study II: research population

The second study was generally independent of the first and focused on the major types of NSCLC, adenocarcinoma and squamous cell carcinoma. Table 4a describes the type and staging profile of the cancer groups.

TABLE 4a

Study II: type and staging of LC samples

As depicted in table 4b, the control group in this study was defined to be more specific to certain samples of smokers and non-smokers. A lung function test was performed on each patient (spirometry, Miller, m.r., et al, eur. respir. j.26(2005) 319-. Samples were included in the control group only if the results of the lung function test of the donor were within the normal range.

TABLE 4b

Study II: composition of control group

According to the stage of UICC	Number of samples
		Smoker's mouth	30
Person giving up smoking	6
		Non-smokers	24

Example 7

Study II: sensitivity of Single marker

The sensitivity of each marker was calculated as before based on the samples described in table 4 b. Sensitivity (expressed as a percentage) was derived at a common 95% specificity level for each individual tested marker. Table 5 describes the sensitivity of adenocarcinoma and squamous cell carcinoma, respectively, as well as the overall sensitivity of each marker.

TABLE 5

Study II: sensitivity of Single marker at 95% specificity (%)

Marker substance	NNMT	CEA	NSE	CYFRA21-1
					Adenocarcinoma	80	43	37	63
Squamous cell carcinoma	87	17	30	80
					Total lung cancer	83	30	33	72

Clearly, NNMT is superior to Cyfra21-1 for two reasons in this case: first the overall sensitivity of NNMT is significantly better. Secondly, and also very importantly, Cyfra21-1 has lower efficacy in adapting to adenocarcinoma than squamous cell carcinoma, while NNMT behaves similarly for both types of cancer.

Example 8

Study II: marker combinations

The results of the marker combinations in study II are shown in Table 6. The type of combination is the same as described in study I above.

TABLE 6

Study II: marker combinations were shown on two major subtypes of lung cancer. Sensitivity given at 95% specificity

Combining any two tested markers results in a significant improvement in the sensitivity of the marker combination compared to that obtained when the markers were used alone (for clarity, see marker set #7 vs #1 and # 2). Some benefit was obtained by adding a third marker (ref, #8 vs # 10). However, the combination of all four markers (set #14) gave no more sensitivity performance than the best triple combination (set # 10).

Duplex and triplex combinations comprising NNMT are always better than the corresponding markers with NNMT replaced by any other marker.

In summary, NNMT alone demonstrated good sensitivity in detecting two major types of NSC lung cancer. The use of NNMT in combination with Cyfra21-1 and/or CEA, respectively, gives very impressive results. Using a combination comprising all three markers, 93% of the cancer samples and 95% of the control samples were correctly classified.

Claims (9)

1. Use of an antibody to nicotinamide N-methyltransferase (NNMT) for the preparation of a kit for use in a method for the in vitro assessment of lung cancer, which method comprises detecting in a sample

a) Protein concentration of nicotinamide N-methyltransferase (NNMT),

b) optionally the protein concentration of one or more other lung cancer markers, and

c) using the concentrations determined in step (a) and optionally step (b) in the assessment of lung cancer, wherein an increase in NNMT is indicative of lung cancer.

2. Use according to claim 1, wherein the one or more other marker is selected from the group consisting of CYFRA21-1, CEA, NSE, and SCC.

3. Use according to claim 2, wherein the marker is CYFRA 21-1.

4. Use according to claim 2, wherein the marker is CEA.

5. Use according to claim 2, wherein the marker is SCC.

Use of an antibody to NNMT in the preparation of a kit for the assessment of lung cancer.

7. Use of a marker panel comprising NNMT and one or more other markers suitable for lung cancer in the manufacture of a kit for the assessment of lung cancer.

8. Use according to claim 7, wherein the one or more other marker is selected from the group consisting of CYFRA21-1, CEA, NSE and SCC.

9. Use according to claim 8, wherein the marker panel comprises at least NNMT and CYFRA 21-1.