MULTIPLE BIOMARKERS FOR DIAGNOSING LUNG CANCER AND USE THEREOF

Abstract

The present invention relates to a composition for diagnosing lung cancer including a preparation capable of measuring expression levels of lung cancer-specific biomarkers SAA, OPN, and CEA at the same time, a kit for diagnosing lung cancer including the same, and a method of diagnosing lung cancer using the composition. The composition for diagnosing lung cancer has effects such as enhanced sensitivity and specificity as compared to conventional biomarkers, thereby exhibiting high diagnostic efficiency.

Claims

1. A composition for diagnosing lung cancer comprising a preparation for measuring an expression level of mRNAs of serum amyloid A (SAA), osteopontin (OPN) and carcinoembryonic antigen (CEA) genes, or proteins thereof.

2. The composition according to claim 1, wherein the preparation for measuring an expression level of the mRNAs of the genes is a primer pair, a probe, or an anti-sense nucleotide, which specifically binds to the mRNAs.

3. The composition according to claim 1, wherein the preparation for measuring an expression level of the proteins is an antibody or an antigen-binding fragment which specifically binds to the proteins or protein-derived fragments.

4. A kit for diagnosing lung cancer comprising the composition defined in claim 1.

5. A method of providing information for diagnosis of lung cancer, comprising: screening a biological specimen from a subject suspected of having lung cancer; measuring expression levels of serum amyloid A (SAA), osteopontin (OPN) and carcinoembryonic antigen (CEA) genes, or proteins encoded by the genes or protein-derived fragments, from the specimen; and comparing the expression level of the genes, or the proteins encoded by the genes or the protein-derived fragments with that of the normal control.

6. The method according to claim 5, wherein the expression level of mRNAs is measured using a method selected from a reverse transcription-polymerase chain reaction (RT-PCR), a competitive reverse transcription-polymerase chain reaction (competitive RT-PCR), a real-time quantitative reverse transcription-polymerase chain reaction (real-time quantitative RT-PCR), a quantitative reverse transcription-polymerase chain reaction (quantitative RT-PCR), an RNase protection assay, Northern blotting, or a DNA chip array.

7. The method according to claim 5, wherein the expression level of the proteins or protein-derived fragments is measured using a method selected from an enzyme-linked immunosorbent assay, Western blotting, an immunohistochemical staining assay, an immunoprecipitation assay, a complement fixation assay, an immunofluorescence test, a radioimmunosorbent test, or mass spectrometry.

8. A kit for diagnosing lung cancer comprising the composition defined in claim 2.

9. A kit for diagnosing lung cancer comprising the composition defined in claim 3.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0021] FIG. 1 shows the Box Plot results of comparing expression levels of SAA, OPN and CEA proteins from biological specimens of a normal person and a lung cancer patient;

[0022] FIG. 2 shows the results of determining abilities to diagnose lung cancer using each of the SAA, OPN and CEA proteins in the biological specimen of the lung cancer patient as a single biomarker, which are indicated as a receiver operating characteristic (ROC) curve;

[0023] FIG. 3 shows the results of determining abilities to diagnose lung cancer using a combination of contents of the SAA, OPN and CEA proteins in the biological specimen of the lung cancer patient as the biomarker, which are indicated using Box Plot and an ROC curve;

[0024] FIG. 4 shows the results of determining abilities to diagnose lung cancer in the lung cancer patient with individual stages using combined proteins according to the present invention, which are indicated by Box Plot;

[0025] FIG. 5 shows the results of determining abilities to diagnose lung cancer in the lung cancer patient with individual stages using the combined proteins according to the present invention, which are indicated as an ROC curve;

[0026] FIG. 6 shows the results of determining diagnostic performance for proteins in the lung cancer patient's serum using the multiple biomarkers of the present invention, which are determined using an ROC curve and an area under the ROC curve (AUC) value; and

[0027] FIG. 7 shows the results of determining diagnostic performance of the single biomarkers for diagnosing lung cancer, which are determined using an ROC curve and an AUC value.

MODE FOR THE INVENTION

[0028] Hereinafter, the present invention will be described in detail.

[0029] In this specification, the term “serum amyloid A” (hereinafter referred to as “SAA”) is known as a precursor of an inflammatory condition of reactive amyloidosis, and thus refers to a main acute-phase protein whose serum concentration increases in various body conditions. The SAA multigene family includes four similar genes. It is known that, among proteins encoded by the genes, SAA1 and SAA2 are main proteins that appear in the acute-phase response, SAA3 is a pseudogene, and SAA4 is constitutively expressed. According to one embodiment of the present invention, an expression level of the SAA1 protein in a patient's blood was determined using an antibody against the protein as a target.

[0030] In this specification, the term “osteopontin” (hereinafter referred to as “OPN”) is a protein that is commonly found in interstitial fluid, and is expressed in activated T cells or plasmacytoid dendritic cells (DCs), which is regulated by a transcription factor (i.e., T-bet). Osteopontin was first known to be a non-collagenous bone matrix protein, since then, it has been known to play an important role in the regulation of cytokine secretion or cellular migration in the immune system, and the like. In particular, osteopontin is a factor that is very critical in differentiating CD4 T cells into TH1 in an immune response, and is also known to play an important role in the regulation of the macrophage activity and the migration of neutrophils into a site of inflammation.

[0031] In this specification, the term “carcinoembryonic antigen” (hereinafter referred to as “CEA”) refers to one example of a carcinoembryonic protein that is a tumor antigen in a prenatal period, and is often used as a tumor indicator. CEA is a substance that originally is normally present in the fetal intestinal tissue, but is secreted into blood when cancer develops in adults. CEA increases due to colon cancer, pancreatic cancer, liver diseases or various benign diseases, smoking or alcohol drinking, and the like.

[0032] In this specification, the expression “biomarker for diagnosing lung cancer” refers to an organic biomolecular material that may be used to diagnose lung cancer because its expression level increases or decreases in cells, tissues, or the like from lung cancer, compared to normal cells. In the present invention, all of SAA, OPN and CEA are used as biomarkers, and their expression levels may be determined at an mRNA or protein level to identify the onset and stage of lung cancer. The biomarkers of the present invention show both improved specificity and sensitivity compared to the single biomarker, and have a characteristic of very high diagnostic efficiency for lung cancer.

[0033] As will be seen from one embodiment of the present invention, the multiple biomarkers of the present invention are selected by combining multiple biomarkers having an excellent diagnostic ability from a number of single biomarkers using a T-test. According to one embodiment of the present invention, performance of the combination of multiple biomarkers is assessed through regression analysis. The diagnostic performance of the composition for diagnosing lung cancer, which is used to detect the multiple biomarkers of the present invention, is determined according to the following equation, which indicates that the multiple biomarkers have a very high diagnostic ability, compared to when the lung cancer is diagnosed using the respective single biomarkers.

x=α+β.sub.1×(SAA Con.)+β.sub.2×(OPN Con.)+β.sub.3×(CEA Con.)  [Equation 1]

[0034] wherein a is in a range of −50≤α≤10, β.sub.1 is in a range of −5≤β.sub.1≤20, β2 is in a range of −5≤β.sub.2≤10, and β.sub.3 is in a range of −10β.sub.3≤5.

[0035] The quantitative analysis data of the three proteins used in the present invention is used to select the multiple combined biomarkers having a considerably improved diagnostic ability compared to the performance as the single biomarkers, and the regression analysis model of Equation 1 is used to maximize an effect of a combination of the multiple biomarkers using the optimum weighted value for a variable for each biomarker. Based on the analysis as described above, the multiple biomarkers of a combination of SAA, OPN and CEA according to the present invention may be used as markers having an excellent ability to diagnose lung cancer.

[0036] Hereinafter, the present invention will be described in detail with reference to embodiments thereof in order to describe this specification in detail. However, it should be understood that the embodiments according to this specification may be modified into various other forms, and are not construed to limit the scope of this specification. The embodiments of this specification are provided to those with ordinary skill in the art in order to describe the present invention more completely.

Experimental Example 1: Collection of Blood Samples of Experimental Group and Control

[0037] Serum samples of lung cancer patients (1,129) and normal persons (700) were provided from Seoul National University Bundang Hospital, Samsung Medical Center, and Kyungpook National University Hospital, and analyzed. The lung cancer patients ranged from 20 to 85 years old (65 years on average), the normal persons ranged from 21 to 87 years old (51 years on average), and the lung cancer patients were distributed by stages: stages 1 and 2: 371, stage 3: 253, and stage 4: 316. The serum samples were obtained by taking peripheral blood from the lung cancer patients and the normal persons, storing the peripheral blood at room temperature for an hour, and centrifuging the peripheral blood to obtain a supernatant. The supernatant was stored at −80° C. until use.

Experimental Example 2: Separation of Proteins in Serum

[0038] A concentration of each of three protein biomarkers (SAA, OPN, and CEA) in the serum collected in Experimental Example 1 was measured using an ELISA method. The SAA and OPN proteins were measured using a Duoset ELISA kit from R&D System, and the CEA protein was measured using an antibody from Biospecific Inc. The analyses were performed in a 96-well plate according to the respective protocols.

[0039] Wells of the 96-well plate were coated overnight with capture antibodies for SAA, OPN, and CEA at 4° C., and then a standard material or a serum were added into each well, and reacted for an hour. Thereafter, the reaction solution was treated with a biotin-tagged detector antibody, and reacted for an hour. Then, a streptavidin-horseradish peroxidase conjugate was added thereto, and the resulting mixture was reacted at room temperature for 30 minutes. 3,3′,5,5′-tetramethylbenzidine (TMB) was added to induce a chromogenic reaction. After 15 minutes, the reaction was stopped with sulfuric acid, and the absorbance at 450 nm was measured using a microplate reader.

[0040] The results of absorbance measurement were analyzed by 5-parameter curve fitting.

[0041] The information on the ELISA kit, the antibodies, and the standard material used in this Example are listed in Table 1 below.

TABLE-US-00001 TABLE 1 Biomarker Product Name Manufacturer SAA Human Serum Amyloid A1 DuoSet R&D Systems ELISA OPN Human Osteopontin (OPN) DuoSet R&D Systems ELISA CEA CEA monoclonal antibody Biospecific Inc. CEA protein Fitzgerald

Experimental Example 3: Selection of Biomarkers for Diagnosing Lung Cancer

[0042] Biomarkers capable of being usefully used for diagnosis of lung cancer by specifically showing a change in values in the lung cancer patients' blood were screened. Based on the results analyzed in Experimental Examples 1 and 2, it can be seen that the expression levels of the SAA, OPN, and CEA proteins were remarkably high in the group of lung cancer patients, compared to the group of normal persons (FIG. 1)

[0043] Each of the identified biomarkers was assessed for an ability to diagnose lung cancer. Based on the sensitivity and specificity measured to diagnose actual lung cancer patients with lung cancer, each of the biomarkers was actually checked for having a diagnostic ability via the ROC curve. As a result, it can be seen that the AUC values were 0.8484, 0.7903, and 0.8056 for the SAA, OPN, and CEA biomarkers, respectively (FIG. 2).

Experimental Example 4: Evaluation of Ability of Biomarker Combination to Diagnose Lung Cancer

[0044] The three biomarkers screened through the T-test were combined to assess a diagnostic ability for lung cancer using regression analysis. An algorithm for performance evaluation uses the following equation.

x=α+β.sub.1×(SAA Con.)+β.sub.2×(OPN Con.)+β.sub.3×(CEA Con.)  [Equation 1]

[0045] wherein a is in a range of −50≤α≤10, β.sub.1 is in a range of −5≤β.sub.1≤20, β2 is in a range of −5≤β.sub.2≤10, and β.sub.3 is in a range of −10β.sub.3<5.

[0046] The algorithm is an equation for calculating an estimated value (X) of a probability of being diagnosed with lung cancer, and was used as an index value to calculate estimated values (X) of probability of being likely to be lung cancer compared to the normal conditions and classify the respective estimated values (X) into normal and lung cancer categories. The estimated values (X) are in a range of −Δ∞8 values. In this case, the closer to −∞8, the more likely to be diagnosed with a normal condition and the closer to ∞8, the more likely to be diagnosed with lung cancer. β.sub.1, β.sub.2, and β.sub.3 are regression coefficients representing weights for concentration values of the SAA, OPN, and CEA proteins, respectively, and a represents an intercept value when the estimated value (X) is 0.

[0047] The diagnostic ability of the combination of three biomarkers according to the present invention was assessed using the algorithm. As a result, it was confirmed that the combination of biomarkers showed excellent diagnostic performance with a specificity of 90%, a sensitivity of 90% or more, and an AUC of 0.9558 (see Table 2 and FIG. 3 below)

TABLE-US-00002 TABLE 2 Sensitivity 99% 98% 95% 90% 85% 80% Specificity 58% 70% 84% 90% 92% 94%

[0048] Also, the lung cancer patients in the experimental group were classified by stage (stages 1 and 2, stage 3, and stage 4), and then their ROC curves were analyzed using the same combination of biomarkers. As a result, it can be seen that the combination of biomarkers had a high AUC value in the terminal lung cancer patients as well as the early (stages 1 and 2) lung cancer patients, that is, that the combination of biomarkers had both high sensitivity and specificity (FIG. 5).

TABLE-US-00003 TABLE 3 Specificity 99% 98% 95% 90% 85% 80% Sensitivity Stages 1 and 2 45% 57% 71% 80% 82% 83% Stage 3 69% 76% 89% 94% 95% 96% Stage 4 68% 79% 90% 94% 97% 97%

Experimental Example 5: Confirmation of Clinical Significance of Diagnostic Kit Using Multiple Biomarkers

[0049] A kit for diagnosing lung cancer (PROTAN LC-Check FL) was manufactured from the identified ability of the combination of biomarkers to diagnose lung cancer. To determine whether a combination of SAA, OPN, and CEA markers in the developed kit shows statistical significance between lung cancer patients and normal persons, proteins in the patients' sera were isolated in the same manner as in Experimental Example 2, and analyzed using bioinformatics and a statistical analysis method (i.e., R statistical package). The statistical analysis was performed using a logistic regression model, and the performance of prediction results for verification of the combination of biomarkers was confirmed by an AUC value of the ROC curve. This was used to deduce an algorithm for indicating a risk of lung cancer, and this algorithm was used to evaluate diagnostic performance for lung cancer.

[0050] As a result, as shown in Table 4 and FIG. 6 below, it was confirmed that the diagnostic kit of the present invention had excellent performance with a specificity of 85%, a sensitivity of 87%, and an AUC of 0.9228.

TABLE-US-00004 TABLE 4 Sensitivity 99% 98% 95% 90% 85% 80% Specificity 17% 39% 61% 81% 87% 89%

[0051] Also, the same experiment was performed using a commercially available single biomarker (Cyfra21-1) to compare the diagnostic performance of the diagnostic kit according to the present invention. As a result, it was confirmed that the AUC of the Cyfra21-1 was measured to be 0.6424, indicating that the diagnostic kit of the present invention showed significantly superior performance (FIG. 7).

[0052] Until now, the present invention has been shown and described with reference to preferred embodiments thereof. It will be understood by those skilled in the art to which the present invention pertains that various changes in form and details may be made therein without departing from the spirit and scope of the present invention. Therefore, the preferred embodiments of the present invention should be considered in a descriptive sense only and not for purposes of limitation. Accordingly, it is clear that the scope of the present invention is defined not by the detailed description of the present invention but by the appended claims, and all differences within an equivalent range should be construed as being included in the present invention.