DNA methylation markers for lung cancer diagnosis and their applications

Abstract

The present invention relates to a DNA methylation marker for diagnosing lung cancer and its use, more specifically to a combination of DNA methylation markers that can determine the presence or absence of lung cancer and its use. The DNA methylation marker for diagnosing lung cancer according to the present invention can diagnose lung cancer with high accuracy using only DNA methylation information from blood samples without using lung cancer tissue samples, and can therefore be useful for the early diagnosis of lung cancer.

Description

[Technical Field]

[0001] The present invention relates to DNA methylation markers for diagnosing lung cancer and uses thereof, and more specifically to a combination of DNA methylation markers that can determine the presence or absence of lung cancer and uses thereof.

[0002] [Background technology]

[0003] Lung cancer is the most common cause of cancer-related death worldwide. Despite significant advances in lung cancer detection and treatment over the past 20 years, the prognosis for patients with the disease remains poor, with the combined 5-year survival rate for all stages of lung cancer ranging from 10% to 15%. The poor prognosis for lung cancer patients is the result of some recurrence, which occurs in approximately 20% to 50% of patients who undergo curative surgical resection with appropriate lymph node dissection. Even for patients with stage 1 lung cancer, the cancer may have spread to nearby lymph nodes or other areas of the body before detection, resulting in a 5-year survival rate of less than 50%.

[0004] Lung cancer is classified into two types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). NSCLC is further classified into various histological types, including squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and adenosquamous carcinoma. Because small cell carcinoma has a poorer postoperative prognosis than NSCLC, anticancer drugs are typically used as the primary treatment. NSCLC generally requires surgery for stage 1 or 2, while anticancer drugs are used for more advanced cases. NSCLC, which accounts for approximately 80% of lung cancers, is staged and treated based on the size of the tumor mass, whether it has penetrated surrounding tissues, the extent of lymph node involvement, and whether it has metastasized to distant organs. However, NSCLC is one of the most difficult cancers to cure, as treatments other than surgery are ineffective. The poor prognosis and high mortality rate are due to a lack of effective diagnostic agents or methods for lung cancer. Therefore, early diagnosis and treatment of lung cancer, which is an incurable disease, are extremely important.

[0005] To accurately diagnose cancer, it is important not only to identify mutated genes but also to understand the mechanisms by which those mutations manifest. Previously, research focused on mutations in the coding sequences of genes, i.e., minute changes such as point mutations, deletions, and insertions, as well as macroscopic chromosomal abnormalities. However, recent studies have shown that extragenic changes are just as important, such as promoter CpG island methylation.

[0006] In addition to A, C, G, and T, mammalian genomic DNA contains a fifth base: 5-methylcytosine (5-mC), in which a methyl group is attached to the fifth carbon of the cytosine ring. 5-mC is always attached exclusively to the C of a CG dinucleotide (5'-mCG-3'), and this type of CG is commonly referred to as CpG. Most Cs in CpGs are methylated. This CpG methylation suppresses the expression of repetitive sequences in the genome, such as alu and transposons, and is the most common site of extragenic variation in mammalian cells. The 5-mC in these CpGs naturally deaminates to T, resulting in CpGs occurring at a frequency of only 1% in mammalian genomes, far lower than their normal frequency (1 / 4 x 1 / 4 = 6.25%).

[0007] CpGs are found in exceptionally densely packed regions called CpG islands. CpG islands are 0.2 kb to 3 kb in length, have a distribution percentage of C and G bases exceeding 50%, and are highly concentrated regions with a CpG distribution percentage of 3.75% or higher. Approximately 45,000 CpG islands occur throughout the human genome, and are particularly concentrated in promoter regions that regulate gene expression. In fact, CpG islands appear in the promoters of important genes (housekeeping genes), which account for approximately half of the human genome (Cross, S. et al., Curr. Opin. Gene Develop., 5:309, 1995). Therefore, active efforts have recently been made to investigate promoter methylation of tumor-related genes in blood, sputum, saliva, feces, and urine, with the aim of using this information in the diagnosis and treatment of various cancers.

[0008] Currently, clinical cancer diagnosis involves a medical history, physical examination, and clinical pathology test. If cancer is suspected, radiological and endoscopic examinations are performed, and finally, a tissue examination is performed to confirm the diagnosis. However, current clinical testing methods can only diagnose cancer when the cancer cell count reaches 1 billion and the tumor reaches 1 cm or more in diameter. In this case, the cancer cells already have the ability to metastasize, and in fact, more than half of cancers have already metastasized. Meanwhile, tumor markers, which detect substances produced directly or indirectly by cancer in the blood, are used for cancer screening. However, these tests have limited accuracy and often test positive even when cancer is not present, leading to confusion. Furthermore, anticancer drugs, which are primarily used to treat cancer, have the problem of only being effective when the tumor volume is small.

[0009] Recently, various methods for diagnosing cancer through DNA methylation have been proposed. DNA methylation occurs primarily at cytosines in CpG islands in the promoter region of specific genes, preventing transcription factor binding and silencing the expression of the specific gene. This is a major mechanism by which genes lose their function in vivo, even in the absence of mutations in the gene's protein-coding sequence, and is believed to be the cause of the loss of function of many tumor suppressor genes in human cancers. While there is debate as to whether promoter CpG island methylation directly induces carcinogenesis or is a secondary change leading to carcinogenesis, abnormal methylation / demethylation in CpG islands has been reported in various cancer cells, including prostate, colon, uterine, and breast cancers. Therefore, this method can be used in a variety of areas, including early cancer diagnosis, cancer risk prediction, cancer prognosis prediction, post-treatment follow-up, and response prediction to anticancer therapy. Recently, there have been active attempts to test this using methods such as methylation-specific PCR (hereinafter referred to as MSP), automated base analysis, or bisulfite pyrosequencing and use it for cancer diagnosis and screening. However, most of these methods are limited to detecting and analyzing the methylation of a small number of specific genes or promoter sites (e.g., Korean Patent No. 1557183, Korean Patent No. 1191947), and therefore have limitations in the efficiency and accuracy of diagnosis.

[0010] Therefore, the present inventors have made extensive efforts to solve the above problems and develop a highly sensitive and accurate DNA methylation marker for diagnosing lung cancer. As a result, they have confirmed that the presence or absence of lung cancer can be diagnosed early with high accuracy by selecting methylated DNA regions from TCGA methylation data of lung cancer tissue samples and lung cancer patient tissues and cfDNA, and then selecting lung cancer-specific DNA methylation markers in common regions from the datasets, and analyzing the DNA methylation markers. This has led to the completion of the present invention.

[0011]

[0012] Summary of the Invention [Problem to be solved by the invention]

[0013] An object of the present invention is to provide a combination of DNA methylation markers for diagnosing lung cancer.

[0014] Another object of the present invention is to provide a method for providing information for diagnosing lung cancer using the DNA methylation marker combination.

[0015] Another object of the present invention is to provide a method for diagnosing lung cancer using the above DNA methylation marker combination.

[0016] It is yet another object of the present invention to provide a probe composition, a primer composition, and a lung cancer diagnostic kit containing the composition, which can detect the DNA methylation marker combination. [Means for solving the problem]

[0017] To achieve the above object, the present invention provides a DNA methylation marker combination for diagnosing lung cancer, which includes the DNA methylation markers shown in Table 1.

[0018] The present invention also provides a method for providing information for diagnosing lung cancer, comprising: (a) isolating DNA from a biological sample; (b) detecting the methylation level of the DNA methylation marker combination; and (c) determining that the patient has lung cancer if the detected DNA methylation marker level exceeds a cut-off value.

[0019] The present invention also provides a method for diagnosing lung cancer, comprising: (a) isolating DNA from a biological sample; (b) detecting the methylation level of the DNA methylation marker combination; and (c) determining that the patient has lung cancer if the detected DNA methylation marker level exceeds a reference value.

[0020] The present invention also provides a composition for diagnosing lung cancer, comprising a primer combination capable of amplifying each of the DNA methylation markers in the DNA methylation marker combination.

[0021] The present invention also provides a lung cancer diagnostic composition comprising a probe combination that can specifically hybridize to a polynucleotide containing 10 or more consecutive bases containing the methylated bases of the DNA methylation markers of the DNA methylation marker combination, or to its complementary polynucleotide.

[0022] The present invention also provides a kit for diagnosing lung cancer, which comprises the composition.

[0023] [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a flowchart showing the process of selecting DNA methylation markers for diagnosing lung cancer according to the present invention.

[0025] [Figure 2] 1 shows ROC curve results confirming the lung cancer diagnostic performance of 366 lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0026] [Figure 3] 1 is a graph confirming the performance of 366 lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0027] [Figure 4] 1 is a flowchart showing the process of selecting a minimal combination of DNA methylation markers for diagnosing lung cancer according to the present invention.

[0028] [Figure 5] 10A and 10B are graphs showing the difference in AUC values ​​between lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention and other candidate marker sets, where (A) shows the results for a combination of 10 hypermethylated markers, (B) shows the results for a combination of 10 hypomethylated markers, (C) shows the results for a combination of 7 hypermethylated markers, and (D) shows the results for a combination of 7 hypomethylated markers.

[0029] [Figure 6] 1 is a ROC_AUC graph showing the results of determining the presence or absence of lung cancer using the minimum combination of lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0030] [Figure 7] 1 shows the results of measuring the difference in methylation level between lung cancer and normal tissues for each of the lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0031] [Figure 8] 1 is a ROC_AUC graph showing the results of determining the presence or absence of lung cancer in clinical samples using a combination of 366 lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0032] [Figure 9]1 is a graph showing the performance of 366 lung cancer-specific DNA methylation markers selected according to an embodiment of the present invention, confirmed in clinical samples.

[0033] [Figure 10] 1 is a ROC_AUC graph showing the results of determining the presence or absence of lung cancer in clinical samples using a combination of 20 lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0034] [Figure 11] 1 is a graph showing the results of confirming the performance of 20 lung cancer-specific DNA methylation markers selected according to an embodiment of the present invention using clinical samples.

[0035] [Figure 12] 1 is a ROC_AUC graph showing the results of determining the presence or absence of lung cancer in clinical samples using a combination of 14 lung cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0036] [Figure 13] 1 is a graph showing the performance of 14 lung cancer-specific DNA methylation markers selected according to an embodiment of the present invention, confirmed in clinical samples.

[0037]

[0038] DETAILED DESCRIPTION OF THE INVENTION

[0039] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention belongs. Generally, the nomenclature used herein and the experimental procedures described below are those well known and commonly used in the art.

[0040]

[0041] In the present invention, we have developed a model that can diagnose the presence or absence of lung cancer using methylation information of cell-free nucleic acids in blood, and have attempted to confirm its accuracy.

[0042] In this study, we combined the methylation data of lung cancer tissue samples listed in the TCGA database with the methylation data of cell-free nucleic acids extracted from tissue and blood samples of lung cancer patients to select DNA methylation markers that can determine the presence or absence of lung cancer.

[0043] Specifically, in one embodiment of the present invention, lung cancer-specific methylation regions were selected based on the methylation data of lung cancer tissue samples and normal samples listed in the TCGA database. Methylated DNA extracted from the tissues and blood of lung cancer patients and normal individuals was sequenced using two methods (cfMeDIP-seq and EM-seq), and the results were compared to identify lung cancer tissue-specific methylation regions. Furthermore, overlapping regions between the methylation regions selected in the TCGA database and those selected in lung cancer patient tissues and normal individual tissues were selected, and overlapping regions between the methylation regions selected in the TCGA database and those selected in lung cancer patient blood and normal individual blood were selected. The overlapping regions in all datasets were initially selected, and then filtered to select final markers. It was confirmed that the use of these markers to determine the presence or absence of lung cancer could be performed with high accuracy (Figure 2).

[0044] Thus, in one aspect, the present invention provides a method for manufacturing a semiconductor device comprising:

[0045] The present invention relates to a combination of DNA methylation markers for diagnosing lung cancer, which comprises two or more DNA methylation markers selected from the group consisting of the DNA methylation markers shown in Table 1 below.

[0046] [Table 1]

[0047] In the present invention, the DNA methylation marker combination for diagnosing lung cancer may be characterized by further including the DNA markers shown in Table 2 below, but is not limited thereto.

[0048] [Table 2]

[0049] In the present invention, the DNA methylation marker combination for diagnosing lung cancer may further include, but is not limited to, two or more DNA methylation markers selected from the group consisting of the DNA markers shown in Table 3 below.

[0050] [Table 3] TIFF2025535972000005.tif253142TIFF2025535972000006.tif253142TIFF2025535972000007.tif253142TIFF2025535972000008.tif37142

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] In the present invention, the term "DNA methylation" refers to the covalent binding of a methyl group to the C5 position of a cytosine base in genomic DNA. The methylation level refers to the amount of methylation present in a DNA base sequence, for example, in all genomic regions and some non-genomic regions, and in the present invention refers to the degree of methylation of the DNA methylation marker. Methylation in the DNA methylation marker can occur over the entire sequence or a portion of the sequence.

[0057] In the present invention, lung cancer refers to malignant tumors that develop in the lungs, and more specifically, refers to primary lung cancer that originates in tissues that constitute the lungs (such as bronchi, bronchioles, and alveoli), and metastatic lung cancer that originates in other organs and metastasizes to the lungs. Primary lung cancer can be comprised of non-small cell lung cancer and small cell carcinoma. Furthermore, non-small cell lung cancer can be comprised of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

[0058]

[0059] In another aspect, the present invention provides

[0060] (a) isolating DNA from a biological sample;

[0061] (b) detecting the methylation level of the DNA methylation marker combination; and

[0062] (c) determining that the subject has lung cancer when the detected DNA methylation marker level exceeds a reference value;

[0063] The present invention relates to a method for providing information for diagnosing lung cancer, comprising:

[0064]

[0065] In still another aspect, the present invention provides

[0066] (a) isolating DNA from a biological sample;

[0067] (b) detecting the methylation level of the DNA methylation marker combination; and

[0068] (c) determining that the subject has lung cancer when the detected DNA methylation marker level exceeds a reference value;

[0069] The present invention relates to a method for diagnosing lung cancer, comprising:

[0070]

[0071] In the present invention, the DNA can be any DNA extracted from a biological sample without limitation, and may be, but is not limited to, cell-free nucleic acid or a fragment of intracellular nucleic acid.

[0072] In the present invention, the biological sample refers to any substance, biological fluid, tissue, or cell obtained from or derived from an individual, such as whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, blood (including plasma and serum), sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, pelvic fluids, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic juice, etc. Examples of fluids that may be used include, but are not limited to, blood, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, semen, hair, saliva, urine, buccal cells, placental cells, cerebrospinal fluid, and mixtures thereof.

[0073]

[0074] In the present invention, the detection of the methylation level in step (b) can be performed by various known methods, preferably, but not limited to, bisulfite conversion or methylated DNA immunoprecipitation (MeDIP).

[0075] In the present invention, another method for detecting DNA methylation is a restriction enzyme-based detection method, which uses a methylation restriction enzyme (MRE) to cleave unmethylated nucleic acids or to cleave a specific sequence (recognition site) regardless of whether it is methylated or not, and then analyzes the cleaved sequence using a hybridization method or PCR.

[0076] In the present invention, methods based on bisulfite substitution include Whole-Genome Bisulfite Sequencing (WGBS), Reduced-Representation Bisulfite Sequencing (RRBS), Methylated CpG Tandems Amplification and Sequencing (MCTA-seq), Targeted Bisulfite Sequencing, Methylation Array, and MSP (Methylation-specific PCR).

[0077] In the present invention, methods for enriching and analyzing methylated DNA include MeDIP-seq (Methylated DNA Immunoprecipitation Sequencing) and MBD-seq (Methyl-CpG Binding Domain Protein Capture Sequencing).

[0078] In the present invention, another method for analyzing methylated DNA is 5-hydroxymethylation profiling, examples of which include 5hmC-Seal (hMe-Seal), hmC-CATCH, hMeDIP-seq (Hydroxymethylated DNA Immunoprecipitation Sequencing), and oxidative bisulfite conversion.

[0079] In the present invention, the detection of the methylation level in step (b) may be characterized by using any one method selected from the group consisting of PCR, methylation-specific PCR, real-time methylation-specific PCR, PCR using a methylated DNA-specific binding protein, quantitative PCR, PCR using a methylation-specific PNA, melting curve analysis, DNA chip, pyrosequencing, bisulfite sequencing, and methylation next-generation sequencing, but is not limited thereto.

[0080]

[0081] In the present invention, a next-generation sequencer can be used with any sequencing method known in the art. Sequencing of nucleic acids isolated by a selection method is typically performed using next-generation sequencing (NGS). Next-generation sequencing includes any sequencing method that determines the nucleotide sequence of individual nucleic acid molecules or clonally expanded proxies for individual nucleic acid molecules in a highly similar manner (e.g., 10 or more molecules are sequenced simultaneously). In one embodiment, the relative abundance of nucleic acid species in a library can be estimated by measuring the relative occurrence of their cognate sequences in the data generated by a sequencing experiment. Next-generation sequencing methods are known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, incorporated herein by reference.

[0082] In one embodiment, next-generation sequencing is performed to determine the nucleotide sequence of individual nucleic acid molecules (e.g., Helicos BioSciences' HeliScope Gene Sequencing system and Pacific Biosciences' PacBio RS system). In other embodiments, sequencing, such as massively parallel short-read sequencing (e.g., Solexa sequencers from Illumina Inc., San Diego, CA), which produces more bases of sequence per sequencing unit than other sequencing methods that produce fewer but longer reads, determines the nucleotide sequence of clonally extended proxies for individual nucleic acid molecules (e.g., Solexa sequencers from Illumina Inc., San Diego, CA; 454 Life Sciences (Branford, Connecticut) and Ion Torrent). Other methods or machines for next-generation sequencing include, but are not limited to, those provided by 454 Life Sciences (Branford, Connecticut), Applied Biosystems (Foster City, California; SOLiD sequencer), Helicos Biosciences Corporation (Cambridge, Massachusetts), and emulsion and microfluidic sequencing techniques such as nano-drip (e.g., GnuBio drip).

[0083] Platforms for next-generation sequencing include, but are not limited to, the Roche / 454 Genome Sequencer (GS) FLX system, the Illumina / Solexa Genome Analyzer (GA), the Life / APG Support Oligonucleotide Ligation Detection (SOLiD) system, the Polonator G.007 system, the Helicos BioSciences HeliScope Gene Sequencing system, the Oxford Nanopore Technologies PromethION, GriION, and MinION systems, and the Pacific Biosciences PacBio RS system.

[0084]

[0085] In the present invention, the methylation level in step (b) may be characterized as being expressed as a beta value, but is not limited thereto.

[0086]

[0087] In the present invention, the step (c) may be characterized by being performed by a method including the following steps:

[0088] (ci) calculating the standard deviation and mean value of the beta values ​​obtained from the normal group samples;

[0089] (c-ii) calculating a z-score using the following formula 1;

[0090]

[0091]

number

[0092] (c-ii) calculating the sum, mean, or standard deviation of the calculated z-scores; and

[0093] (c-iii) A step of determining that the patient has lung cancer when the sum, mean, or standard deviation of the calculated z-scores exceeds a reference value.

[0094] In the present invention, the step (c) may be characterized by comparing the methylation level information of the detected DNA methylation marker combination with the value of a normal sample, and determining the presence or absence of lung cancer if the difference is greater than or equal to the reference value, but is not limited thereto.

[0095] In the present invention, the reference value in step (c) can be used without limitation as long as it is a value that can determine the presence or absence of lung cancer, and is preferably 99% to 75%, more preferably 97% to 80%, and most preferably 95% of the methylation level of a normal sample, but is not limited thereto.

[0096] In the present invention, the information on the methylation level of the detected combination of DNA methylation markers in step (c) may be one or more values ​​selected from the group consisting of the sum, difference, product, mean, log product, log sum, median, quantile, minimum, maximum, variance, standard deviation, absolute deviation, coefficient of variation, reciprocal values ​​thereof, and combinations thereof, but is not limited thereto.

[0097] In the present invention, when calculating the methylation level information as a beta value, the beta value of a hypermethylated methylation marker is used as is, while the beta value of a hypomethylated methylation marker is calculated by subtracting it from a certain reference value such as 100 or 1, or by multiplying it by -1, which is well known to those skilled in the art.

[0098]

[0099] In the present invention, the reference values ​​in step (c) can be any value that can determine the presence or absence of lung cancer, and the reference value for the average z-score can be preferably 0.2 to 0.9, more preferably 0.3 to 0.8, and most preferably 0.5 to 0.6. The reference value for the sum of z-scores can be 100 to 300, more preferably 150 to 200, and most preferably 180 to 200. The reference value for the standard deviation of z-scores can be, but is not limited to, 0.5 to 2, more preferably 0.8 to 1.5, and most preferably 0.9 to 1.2.

[0100]

[0101] In still another aspect, the present invention provides

[0102] The present invention relates to a lung cancer diagnostic composition comprising a primer combination capable of amplifying each of the DNA methylation markers in the DNA methylation marker combination.

[0103] In the present invention, the appropriate length of the primer may vary depending on the intended use, but may generally consist of 15 to 30 bases. The primer sequence does not need to be completely complementary to the template, but must be sufficiently complementary to hybridize with the template. The primer can hybridize to a DNA sequence containing a methylation marker and amplify a DNA fragment containing the methylation marker. The primer of the present invention can be used in diagnostic kits and prognostic methods for detecting DNA methylation levels and confirming the presence or absence of lung cancer.

[0104] In the present invention, the primers capable of amplifying the DNA methylation marker can be used without limitation as long as they are of the same chromosomal base sequence that does not directly include the marker region. Specifically, the primers may be 1 bp to 1000 bp 5' upstream and 1 bp to 1000 bp 3' downstream of the marker region, or more specifically, 1 bp to 200 bp 5' upstream and 1 bp to 200 bp 3' downstream of the marker region, but are not limited thereto.

[0105]

[0106] In still another aspect, the present invention provides

[0107] The present invention relates to a lung cancer diagnostic composition comprising a probe combination that can specifically hybridize with a polynucleotide containing 10 or more consecutive bases containing the methylated bases of the DNA methylation markers of the DNA methylation marker combination, or with its complementary polynucleotide.

[0108] In the present invention, the probe may be methylation-specific, meaning that it hybridizes specifically only to methylated nucleic acids in the methylation marker region. Here, hybridization is typically performed under stringent conditions, such as a salt concentration of 1 M or less and a temperature of 25° C. or higher. For example, conditions such as 5X SSPE (750 mM NaCl, 50 mM Na Phosphate, 5 mM EDTA, pH 7.4) and a temperature of 25° C. to 30° C. may be suitable for methylation-specific probe hybridization.

[0109] In the present invention, the probe refers to a hybridization probe, which is an oligonucleotide capable of sequence-specific binding to a complementary strand of nucleic acid. The methylation-specific probe of the present invention may hybridize to DNA fragments derived from one individual but not to fragments derived from the other individual when methylation is present in nucleic acid fragments derived from two individuals of the same species. In this case, the hybridization conditions must be sufficiently stringent to show a significant difference in hybridization intensity, resulting in hybridization depending on the presence or absence of methylation. Preferably, the central region of such a probe of the present invention is aligned with the region of a methylation marker. The probe of the present invention can be used in diagnostic kits and prognostic methods for detecting DNA methylation levels and confirming the presence or absence of lung cancer.

[0110]

[0111] In still another aspect, the present invention provides

[0112] The present invention also relates to a kit for diagnosing lung cancer, which comprises any one of the above compositions.

[0113] In the present invention, the kit may include not only the polynucleotide of the present invention but also one or more other component compositions, solutions, or devices suitable for the analytical method. In one embodiment, the kit of the present invention may be a kit containing essential components necessary for PCR, and may further include test tubes or other suitable containers, a reaction buffer (various pH and magnesium concentrations), deoxynucleotides (dNTPs), enzymes such as Taq polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC water (DEPC-water), and sterile water. In another embodiment, the kit of the present invention may be a kit for predicting blood statin levels containing essential components necessary for DNA chip analysis. The DNA chip kit includes a substrate to which a polynucleotide, primer, or probe specific for the methylation is attached, and the substrate contains a nucleic acid corresponding to a quantitative control gene or a fragment thereof.

[0114]

[0115] [Example]

[0116] The present invention will be described in more detail below through examples. It will be obvious to those skilled in the art that these examples are merely for the purpose of illustrating the present invention and should not be construed as limiting the scope of the present invention.

[0117]

[0118] Example 1. Selection of lung cancer-specific methylated regions using TCGA methylation 450K array data

[0119] Methylation levels were determined using the Infinium Human Methylation 450K BeadChip array data (UCSC Xena, http: / / xena.ucsc.edu) from the Cancer Genome Atlas (TCGA). DNA extracted from tissues was converted through bisulfite treatment, and the presence or absence of DNA methylation was confirmed through the modification of cytosine bases. The methylation level was determined for each region, and differentially methylated regions between lung cancer tissue and surrounding normal tissue were identified using beta values, which indicate the degree of methylation.

[0120] The TCGA methylation 450k array data was divided into lung adenocarcinoma and lung squamous cell carcinoma as shown in Tables 4 and 5, and then divided into a train group and a test group, and marker selection was performed using the train group.

[0121] [Table 4]

[0122] [Table 5]

[0123] First, we removed missing values ​​in approximately 480,000 (480K) regions,

[0124] To select lung adenocarcinoma-specific methylated regions, we used Limma (Linear Models for Microarray Data) software to select regions with an FDR value of less than 0.01 and an absolute delta beta of more than 0.25, and selected 617 hypomethylated CpGs and 3,114 hypermethylated CpGs specific to lung adenocarcinoma.

[0125] To select squamous cell carcinoma-specific methylated regions, we used Limma (Linear Models for Microarray Data) software to select regions with an FDR value of less than 0.01 and an absolute delta beta of more than 0.25, and selected 8,105 hypomethylated CpGs and 5,486 hypermethylated CpGs specific to squamous cell carcinoma of the lung.

[0126]

[0127] Example 2. Extraction of methylated cfDNA from blood and next-generation sequencing (cfMeDIP-Seq)

[0128] Blood samples were collected from 25 lung cancer patients and 190 normal volunteers. The plasma fraction was first centrifuged at 3,000 rpm and 25°C for 10 minutes. The plasma fraction was then centrifuged at 16,000 g and 25°C for 10 minutes to separate the precipitate. Cell-free DNA was extracted from the isolated plasma using the Chemagen DNA kit. Adaptor ligation was then performed using the Truseq Nano DNA HT Library Prep Kit (Illumina). After adapter ligation, 5mC immunoprecipitation was performed using the antibodies in the cfMeDIP Kit (Diagnode) at 10 rpm and 4°C for 17 hours. Purification was then performed, and PCR enrichment was repeated using the Truseq Nano DNA HT Library Prep Kit (Illumina) to generate the final library. The constructed libraries were sequenced using a Novaseq 6000 (Illumina) in 150 paired-end mode, producing approximately 100 million reads per sample.

[0129]

[0130] Example 3. Identification of lung cancer-specific methylated regions through cfMeDIP-Seq data analysis

[0131] In Example 2, methylated cell-free nucleic acids were sequenced, and the obtained nucleic acid fragment data was methylated. This data was aligned to the human reference genome, allowing for identification of methylated regions across the entire human genome. MeDIP-Seq data indicated methylated regions, and differentially methylated regions between the lung cancer group and the normal group were identified using normalized values ​​per 300 bp bin.

[0132] The cfMeDIP-Seq data was divided into a train group and a test group as shown in Table 6 below, and markers were selected using the train group, and the performance of the markers was confirmed using the test group.

[0133] [Table 6]

[0134] First, adapter trimming and quality trimming were performed on the fastq files using Trim Galore (version 0.6.6). Then, the nucleic acid fragment data were aligned to the reference genome (hg19) using the bwa (version 0.7.17-r1188) alignment tool. PCR duplicate nucleic acid fragments were removed using the samtools rmdup (version 1.11) tool. Nucleic acid fragments with a mapping quality of less than 10 were removed using the samtools view (version 1.11) tool. After removing all but chr1-22, X, and Y, the data were binned into 300-bp bins, with no overlapping except for the sex chromosomes, and read counts per 300-bp bin were generated.

[0135] Blacklist regions (Low_mappability_island, centromeric_repeat, etc.) and bins with a sum of read counts of 10 or less across all samples per bin were excluded.

[0136] Normalized values ​​per 300 bp bin (TMM normalized values) were generated using edgeR (Empirical Analysis of Digital Gene Expression Data in R) software.

[0137] Finally, using edgeR software, we selected 120,176 hypermethylated and 185,638 hypomethylated regions specific to lung cancer with an FDR value of less than 0.05 and an absolute log2 fold change of more than 2, and then extracted the CpGs contained in the selected bins.

[0138] Next, we created a model to distinguish between lung cancer and normal groups using bins containing 138 hypermethylated CpGs and 1,309 hypomethylated CpGs that were identically found in the TCGA array data and MeDIP-seq data, and confirmed its performance with an accuracy of 0.98 and AUC of 1.00 in the test group.

[0139]

[0140] Example 4. EM-seq (Enzymatic Methylation Sequencing)

[0141] 4-1. Whole genome EM-seq

[0142] Blood samples were collected from seven lung cancer patients and ten normal subjects. The plasma portion was first centrifuged at 3,000 rpm, 25°C, and 10 minutes. The plasma was then centrifuged again at 16,000 g, 25°C, and 10 minutes to separate the plasma. 400 μl of the buffy coat was then isolated, and genomic DNA was extracted using a QIAmp DNA Mini Kit (Qiagen, Germany).

[0143] In addition, 10 ng to 30 ng of fresh frozen tissue from lung cancer tissues and surrounding normal tissues from the seven lung cancer patients was disrupted using FastPrep-24, and genomic DNA was extracted using a QIAmp DNA Mini Kit (Qiagen, Germany).

[0144] The concentration of genomic DNA extracted from the tissues and blood was measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA). Purity was confirmed using Nanodrop, and then 50 μl of 200 ng of DNA was sheared to 240 bp to 290 bp using Covaris. The DNA size was confirmed using D1000 screen tape and reagent (Agilent, USA) on a Tapestation 4200 (Agilent, USA).

[0145] Methylation conversion was performed on 200 ng of sheared DNA using TET2 (ten-eleven translocation dioxygenase 2) and APOBEC to convert unmethylated cytosine to uracil. Libraries were then prepared using the enzymatic methyl-seq (NEB kit). The concentration and size of the DNA libraries were measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA) and Tapestation 4200 (Agilent, USA), respectively. Sequencing was performed on a Novaseq 6000 (Illumina) in 150 paired-end mode at a final concentration of 2 nM, producing approximately 650 million reads per sample.

[0146]

[0147] 4-2. cfDNA Targeted Methylome Panel-Based EM-Seq

[0148] The methylome panel used was the Twist Human Methylome Panel (Twist Bioscience, USA).

[0149] Blood samples were collected from 12 lung cancer patients, including the seven lung cancer patients described in Example 4-1, and seven normal subjects. The plasma fraction was then centrifuged at 3,000 rpm and 25°C for 10 minutes. The plasma was then centrifuged at 16,000 g and 25°C for 10 minutes to remove the precipitate and separate the supernatant. Cell-free DNA was extracted from the separated plasma using the Mag-bind cfDNA kit, and its concentration was measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA). The maximum amount of extracted cfDNA was subjected to methylation conversion via the substitution of unmethylated cytosine with uracil using TET2 (ten-eleven translocation dioxygenase 2) and APOBEC, followed by the preparation of libraries using the enzyme Methyl-seq (NEB kit).

[0150] The concentration and size of the DNA libraries were measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA) and a Tapestation 4200 (Agilent, USA), respectively. After hybridization, 200 ng of the libraries were pooled into eight samples, and the captured samples were analyzed for concentration using a Tapestation 4200 (Agilent, USA) with High Sensitivity D1000 Screen Tape & Reagent (Agilent, USA). Sequencing was performed using a Novaseq 6000 (Illumina) in 150 paired-end mode, adjusting the final concentration to 2 nM, producing approximately 150 million reads per sample.

[0151]

[0152] Example 5. Identification of lung cancer-specific methylated regions through EM-seq data analysis

[0153] By sequencing methylated cell-free nucleic acids, the obtained nucleic acid fragment data was methylated. This data was aligned to the human reference genome, allowing us to identify each methylated region across the entire human genome. In the EM-Seq data, methylated cytosines remained as cytosines, and unmethylated cytosines were converted to thymine, allowing us to identify the methylated CpGs and the degree of methylation. We then identified differentially methylated CpGs between the lung cancer group and the normal group.

[0154] The EM-seq data was divided into a Discovery group and an External group as shown in Tables 7 and 8 below. Markers were selected using the whole-genome EM-seq data, feature filtering was performed using the External group of the Twist methylome panel, and the presence or absence of cancer was classified using the entire Twist methylome panel data.

[0155] [Table 7]

[0156] [Table 8]

[0157] First, adapter trimming and quality trimming were performed on the fastq files using Trim Galore (version 0.6.6). Then, the nucleic acid fragment data were aligned to the reference genome (hg19) using the Bismark (version 0.23.0) alignment tool. Using the Samtools view (version 1.11) tool, only nucleic acid fragments with a mapping quality of 10 or higher and chr1-22, X, and Y were selected. Methylation calling was then performed using the bismark_methylation_extractor in Bismark (version 0.23.0).

[0158] The beta values ​​(methylation percentage) of the tumor and normal samples were merged into one file using the methylKit (version 1.12.0) R package, and then each CpG with an absolute difference greater than 25 and a q value less than 0.01 was selected using the methylKit (version 1.12.0) R package.

[0159] By using this method, differentially methylated CpGs were identified between lung tissue and surrounding normal tissue, resulting in 72,113 CpGs that were hypermethylated specifically in lung cancer and 260,606 CpGs that were hypomethylated. By using this method, differentially methylated CpGs were identified between normal lung tissue and normal whole blood (WBC), resulting in 108,548 CpGs that were hypermethylated specifically in lung tissue and 387,282 CpGs that were hypomethylated.

[0160] Among the selected CpGs, overlapping CpGs were selected, resulting in the selection of 13,196 hypermethylated CpGs and 54,239 hypomethylated CpGs.

[0161]

[0162] Example 6. Final marker selection

[0163] 138 hypermethylated CpGs and 1,309 hypomethylated CpGs found in the same region were selected in Examples 1 and 3, and 362 hypermethylated CpGs and 101 hypomethylated CpGs found in the same region were selected in Examples 1 and 5, respectively. These were then integrated without overlaps, and 489 hypermethylated CpGs and 1,401 hypomethylated CpGs were selected as the primary integrated region.

[0164] To select lung cancer-specific CpGs from the selected primary comprehensive CpGs, a filtering process based on the external set was performed on the EM-Seq data using the cfDNA target methylome panel in Table 6.

[0165] First, the AUC for distinguishing between the lung cancer group and the normal group was calculated using the beta value of the primary comprehensive CpG, and meaningful CpGs with an AUC of 6.5 or higher were selected.

[0166] Additionally, CpGs were selected that had an absolute difference between the lung cancer group and the normal group of 3 or more, an absolute q value of less than 0.05, and an absolute SD (standard deviation) of the normal group of less than 0.05.

[0167] The CpGs selected by the two filtering steps were combined without overlap, and 130 hypermethylated CpGs and 236 hypomethylated CpGs were finally selected.

[0168] The list of 366 selected methylation markers is shown in Table 9 below.

[0169] [Table 9] TIFF2025535972000016.tif252170TIFF2025535972000017.tif251170TIFF2025535972000018.tif251170TIFF2025535972000019.tif255170 TIFF2025535972000020.tif252170TIFF2025535972000021.tif252170TIFF2025535972000022.tif251170TIFF2025535972000023.tif228170

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182] Example 7. Marker performance confirmation

[0183] The performance of the 366 markers selected in Example 6 was confirmed using the beta values ​​obtained from the entire EM-Seq data. First, the standard deviation and mean values ​​of the normal group samples were calculated, and then the z-scores for each marker were calculated using Equation 1 below.

[0184]

[0185]

number

[0186] The calculated z-scores were used to calculate the mean z-score, sum z-score, and SD z-score values, and if the mean z-score, sum z-score, and SD z-score values ​​exceeded the reference values ​​listed in Table 10, lung cancer was determined.

[0187] The reference values ​​in Table 10 were determined as the 95th percentile values ​​of each normal sample.

[0188] [Table 10]

[0189] As a result, as shown in Table 11, Figures 2 and 3, the AUC values, which are the results of the ROC analysis, were confirmed to be 1.00, 1.00 and 1.00 for the mean of Z-score, the sum of Z-score, and the SD of Z-score, respectively.

[0190] [Table 11]

[0191]

[0192] Example 8. Selection of lung cancer-specific methylated regions using TCGA methylation 450K array data

[0193] Additionally, to derive the minimum marker combination for lung cancer diagnosis, methylation levels were confirmed using the Infinium Human Methylation 450K BeadChip array data (UCSC Xena, http: / / xena.ucsc.edu) in TCGA (The Cancer Genome Atlas). DNA extracted from tissues was converted through bisulfite treatment, and the presence or absence of DNA methylation was confirmed through the modification of cytosine bases. The methylation level was confirmed for each region, and differentially methylated regions between lung cancer tissue and surrounding normal tissue were selected using the beta value, which indicates the degree of methylation.

[0194] The TCGA methylation 450k array data is shown in Table 12.

[0195] [Table 12]

[0196] First, after excluding missing values ​​in approximately 480,000 (480K) regions, we calculated the methylation score to select meaningful hypermethylated regions. The methylation score for hypermethylated regions was calculated by adding the beta values ​​directly, while for hypomethylated regions, the beta values ​​were multiplied by -1 before calculating the sum.

[0197] Ten CpG regions were randomly selected from 1,000 samples to form CpG region sets. Each CpG region set was then used to calculate the methylation scores of 902 lung cancer tissues and surrounding normal tissues. The calculated methylation scores were then used to calculate the AUC for classifying lung cancer tissues from surrounding normal tissues. The CpG regions with the highest AUC were selected, resulting in a set of 10 hypermethylated regions with an AUC of 0.996 and a set of 7 hypomethylated regions with an AUC of 0.990 (Figure 5, A and B).

[0198] In addition, seven regions were randomly selected from 1,000 samples to form a CpG region set, and then a set of seven hypermethylated regions with an AUC of 0.996 and a set of seven hypomethylated regions with an AUC of 0.976 were selected using the same method (Figure 5, C, D, Table 13).

[0199] In addition, it was confirmed that the 20-item and 14-item sets could distinguish lung cancer from surrounding tissues with high accuracy (FIGS. 6 and 7).

[0200] In the table below, O in the nCPG=14 column means a set of 14, and X means a marker that was not selected in the set of 14 but was additionally selected in the set of 20.

[0201] [Table 13]

[0202]

[0203] Example 9. Marker panel performance validation in clinical samples

[0204] Targeted EM-Seq was performed on samples from 129 lung cancer patients and 184 normal subjects in Table 14 to confirm the performance of the marker sets (Tables 9 and 13), respectively.

[0205] [Table 14]

[0206] 9-1. Targeted EM-Seq execution

[0207] Blood samples were collected from the patients and centrifuged at 3,000 rpm for 10 minutes at 25°C to separate the plasma fraction. The resulting plasma was then centrifuged at 16,000 g for 10 minutes at 25°C to remove the precipitate. Cell-free DNA was extracted from the separated plasma using the Mag-bind cfDNA kit, and its concentration was measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA). The maximum amount of extracted cfDNA was methylated using ten-eleven translocation dioxygenase 2 (TET2) and APOBEC to convert unmethylated cytosines to uracils. Libraries were then constructed using the Methyl-seq (NEB) kit.

[0208] The concentration and size of the DNA libraries were measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA) and Tapestation 4200 (Agilent, USA), respectively. Eight 200ng libraries were pooled and hybridized. The captured samples were then measured for concentration using High Sensitivity D1000 Screen Tape & Reagent (Agilent, USA) on a Tapestation 4200 (Agilent, USA). Sequencing was performed using a Miseq Dx (Illumina) instrument in 150 paired-end mode, adjusting the final concentration to 11 pM, yielding 700x depth per sample.

[0209] 9-2.Performance check

[0210] Since methylated cell-free nucleic acids are sequenced, the obtained nucleic acid fragment data is methylated, and by aligning this to the human reference genome, it is possible to identify each methylated region across the entire human genome. In EM-Seq data, methylated cytosine regions remain as cytosines, and unmethylated cytosines are converted to thymine, allowing the methylated regions and degree of methylation to be identified.

[0211] First, adapter trimming and quality trimming were performed on the fastq files using Trim Galore (version 0.6.6), and then the nucleic acid fragment data was aligned to the reference genome (hg19) using the Bismark (version 0.23.0) alignment tool. Using the Samtools view (version 1.11) tool, only nucleic acid fragments with a mapping quality of 10 or higher and chr1-22, X, and Y were selected. Methylation calling was then performed using the bismark_methylation_extractor in Bismark (version 0.23.0).

[0212] The beta values ​​(methylation percentage) of the tumor and normal samples were merged into one file using the methylKit (version 1.12.0) R package, and then the beta values ​​were used to calculate z-scores using Equation 1 based on the standard deviation of the normal group samples derived in Example 7.

[0213] The calculated z-scores were used to calculate the mean z-score, sum z-score, and SD z-score values, and lung cancer was determined when the mean z-score, sum z-score, and SD z-score values ​​exceeded the reference values ​​listed in Table 8. The same criteria as in Example 7 were used as the reference values.

[0214] As a result, when all 366 marker combinations were used, as shown in Table 15, Figures 8 and 9, the AUC values, which are the results of the ROC analysis, were 0.75, 0.75 and 0.80 for the mean Z score, sum Z score and SD Z score, respectively.

[0215] [Table 15]

[0216] When a combination of 20 markers was used, the AUC values, which are the results of the ROC analysis, were 0.78, 0.78, and 0.77 for the mean Z score, the sum of Z scores, and the SD of Z scores, as shown in Table 16, Figures 10 and 11, respectively.

[0217] [Table 16]

[0218] When a combination of 14 markers was used, the AUC values, which are the results of the ROC analysis, were confirmed to be 0.78, 0.78, and 0.77 for the mean Z score, sum Z score, and SD Z score, respectively, as shown in Table 17, Figures 12 and 13.

[0219] [Table 17]

[0220] Although certain parts of the present invention have been described in detail above, it will be apparent to those skilled in the art that these specific descriptions are merely preferred embodiments and do not limit the scope of the present invention. Therefore, the true scope of the present invention is to be defined by the appended claims and their equivalents.

[0221] [Industrial Applicability]

[0222] The DNA methylation marker for diagnosing lung cancer according to the present invention can diagnose lung cancer with high accuracy using only DNA methylation information from blood samples, without using lung cancer tissue samples, and can therefore be useful for the early diagnosis of lung cancer.

Claims

1. A combination of DNA methylation markers for diagnosing lung cancer, comprising the DNA methylation markers shown in Table 1 below. Table 1

2. The combination of DNA methylation markers for diagnosing lung cancer according to claim 1, further comprising DNA markers shown in Table 2 below. 【Table 2】

3. The combination of DNA methylation markers for diagnosing lung cancer according to claim 2, further comprising two or more DNA methylation markers selected from the group consisting of DNA markers shown in Table 3 below. 【Table 3】

4. (a) isolating DNA from a biological sample; (b) detecting the methylation level of the DNA methylation marker combination of claim 1; and (c) determining that the subject has lung cancer when the detected DNA methylation marker level exceeds a reference value; A method for providing information for diagnosing lung cancer, comprising:

5. (a) isolating DNA from a biological sample; (b) detecting the methylation level of the DNA methylation marker combination of claim 1; and (c) determining that the subject has lung cancer when the detected DNA methylation marker level exceeds a reference value; A method for diagnosing lung cancer comprising:

6. 6. The method of claim 4 or 5, wherein the detection of the methylation level in step (b) is performed using any one method selected from the group consisting of PCR, methylation-specific PCR, real-time methylation-specific PCR, PCR using a methylated DNA-specific binding protein, quantitative PCR, PCR using a methylation-specific PNA, melting curve analysis, DNA chip, pyrosequencing, bisulfite sequencing, and methylation next-generation sequencing.

7. The method according to claim 4 or 5, wherein the methylation level in step (b) is expressed as a beta value.

8. 6. The method according to claim 4 or 5, wherein step (c) is carried out by a method comprising the steps of: (ci) calculating the standard deviation and mean of the beta values ​​obtained in the normal group samples; (c-ii) calculating the z-score using the following formula 1; and [Equation 1] (c-ii) calculating the sum, mean or standard deviation of the calculated z-scores; and (c-iii) determining that the subject has lung cancer when the sum, mean, or standard deviation of the calculated z-scores exceeds a reference value.

9. A composition for diagnosing lung cancer, comprising a primer combination capable of amplifying each of the DNA methylation markers in the DNA methylation marker combination according to any one of claims 1 to 3.

10. A lung cancer diagnostic composition comprising a probe combination capable of specifically hybridizing to a polynucleotide comprising 10 or more consecutive bases containing a methylated base of the DNA methylation marker combination of any one of claims 1 to 3, or a complementary polynucleotide thereof.

11. A lung cancer diagnostic kit comprising the composition of claim 9 or 10.

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