Composition for Diagnosing Pancreatic Cancer for Improving Diagnostic Ability of CA19-9 for Pancreatic Cancer

Abstract

The present application relates to a biomarker for diagnosing pancreatic cancer, a composition for diagnosing pancreatic cancer comprising a preparation capable of detecting said biomarker, and a method for diagnosing pancreatic cancer using said biomarker. The composition for diagnosing pancreatic cancer and / or the diagnostic method provided in the present application is characterized by being able to improve pancreatic cancer diagnostic performance compared to the case where CA19-19 is used alone by using CA19-19 together with a predetermined biomarker.

Description

Technology Field

[0001] The present application relates to a biomarker for diagnosing pancreatic cancer, a composition for diagnosing pancreatic cancer comprising a preparation capable of detecting said biomarker, and a method for diagnosing pancreatic cancer using said biomarker. Background Technology

[0003] A biomarker refers to an indicator capable of detecting changes induced within an organism due to external influences, and active research is being conducted on its use to diagnose various diseases, such as cancer and neurological disorders, or to predict the efficacy of specific treatments.

[0004] Meanwhile, the pancreas is an organ located behind the stomach that secretes digestive fluids and hormones and is about 20 cm in length; generally, when referring to pancreatic cancer, it may mean pancreatic ductal cancer. Pancreatic cancer is on the rise as Western-style diets become more common, and it is known to occur more frequently in men with a high mortality rate. Pancreatic cancer is known to be caused by smoking, coffee, alcohol consumption, a meat-heavy diet, a history of diabetes, chronic pancreatitis, and nonpolyposis colorectal cancer syndrome, as well as substances such as beta-naphthylamine and benzidine.

[0005] Pancreatic cancer often presents with few symptoms in the early stages, and symptoms such as pain and weight loss typically appear only after metastasis has already occurred, resulting in a very high mortality rate.

[0006] For more effective treatment of pancreatic cancer, the development of technologies for early diagnosis, identification of the stage of progression, and / or differentiation from other similar diseases is required. The problem to be solved

[0008] A pancreatic cancer diagnostic technology is provided that has improved diagnostic performance compared to the case where CA19-9 is used alone by using CA19-9, an existing pancreatic cancer diagnostic marker, together with one or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, and PTGES.

[0009] One example is,

[0010] (1) CA19-9 and / or its coding gene, and

[0011] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0012] The present invention provides a biomarker for the diagnosis of pancreatic cancer, comprising: a blood sample, e.g., blood (whole blood), plasma, serum and / or buffy coat, etc.

[0013] Another example is,

[0014] (1) CA19-9 and / or its coding gene, and

[0015] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0016] The present invention provides a composition for diagnosing pancreatic cancer or a kit for diagnosing pancreatic cancer comprising a preparation capable of detecting [the substance]. The composition for diagnosing pancreatic cancer or the kit for diagnosing pancreatic cancer may be intended for use in blood samples, e.g., blood (whole blood), plasma, serum, and / or buffy coat.

[0017] Another example is, in a blood sample separated from a subject,

[0018] (1) CA19-9 and / or its coding gene, and

[0019] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0020] A method for diagnosing pancreatic cancer or a method for providing information for the diagnosis of pancreatic cancer is provided, comprising a step of detecting (analyzing). The blood sample may include whole blood, plasma, serum, and / or buffy coat. The step of detecting may include a step of measuring the presence and / or level (e.g., concentration, expression level, etc.) of the biomarker (protein and / or gene (DNA, mRNA, etc.) described above in the blood sample.

[0021] The above pancreatic cancer may include early pancreatic cancer, late pancreatic cancer, or both.

[0022] The diagnostic technology provided in this application can also be applied to the diagnosis of bile duct cancer.

[0023] Accordingly, another example is,

[0024] (1) CA19-9 and / or its coding gene, and

[0025] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0026] The present invention provides a biomarker for the diagnosis of bile duct cancer, comprising: a buffy coat, etc. The biomarker may be for detection or analysis in blood samples, e.g., whole blood, plasma, serum and / or buffy coat.

[0027] Another example is,

[0028] (1) CA19-9 and / or its coding gene, and

[0029] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0030] The present invention provides a composition for diagnosing bile duct cancer or a kit for diagnosing bile duct cancer comprising a preparation capable of detecting [the substance]. The composition for diagnosing bile duct cancer or the kit for diagnosing bile duct cancer may be intended for use in blood samples, e.g., whole blood, plasma, serum, and / or buffy coat.

[0031] Another example is, in a blood sample separated from a subject,

[0032] (1) CA19-9 and / or its coding gene, and

[0033] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0034] A method for diagnosing bile duct cancer or a method for providing information for the diagnosis of bile duct cancer is provided, comprising a step of detecting (analyzing). The blood sample may include blood (whole blood), plasma, serum, and / or buffy coat. The step of detecting may include a step of measuring the presence and / or level (e.g., concentration, expression level, etc.) of the biomarker (protein and / or gene (DNA, mRNA, etc.) described above in the blood sample.

[0036] The biomarker, diagnostic composition, diagnostic kit, and diagnostic method described above may have superior diagnostic efficacy (e.g., accuracy, etc.) for pancreatic cancer (early pancreatic cancer, late pancreatic cancer, or both) and / or biliary tract cancer compared to the case where CA19-9 is used alone.

[0038] Other examples include (1) CA19-9 and / or its coding gene, and

[0039] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0040] A composition for improving (enhancing, increasing) the diagnostic efficacy (accuracy, etc.) of pancreatic cancer and / or biliary tract cancer using CA19-9, comprising a preparation capable of detecting [the substance].

[0041] Another example is, in a blood sample separated from a subject,

[0042] (1) CA19-9 and / or its coding gene, and

[0043] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0044] including a step of detecting (analyzing),

[0045] A method is provided to improve (enhance, increase) the diagnostic efficacy (accuracy, etc.) of pancreatic cancer and / or bile duct cancer using CA19-9. The blood sample may include blood (whole blood), plasma, serum, and / or buffy coat. The detecting step may include measuring the presence and / or level (e.g., concentration, expression level, etc.) of the biomarker (protein and / or gene (DNA, mRNA, etc.) described above in the blood sample. means of solving the problem

[0047] We aim to improve the (early) diagnostic capability of pancreatic cancer by developing a diagnostic technology that considers the levels of CA19-9, a pancreatic cancer-related tumor marker widely used in health checkups, along with the mRNA expression levels of specific biomarkers.

[0049] The inventors have completed the present invention by confirming that by using CA19-9, an existing tumor marker related to pancreatic cancer, together with one or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, and PTGES, the diagnostic performance of pancreatic cancer can be improved compared to the case where CA19-9 is used alone, and that it is possible to diagnose pancreatic cancer not only when the level of CA19-9 in the blood of pancreatic cancer patients is high but also when it is low (e.g., within the normal range).

[0051] The following explains this in more detail:

[0053] Pancreatic diagnosis

[0054] In this specification, "pancreatic cancer" is a general term for tumors occurring in the pancreas, and examples may include benign tumors such as serous cystic tumors, mucinous cystic tumors, intraductal papillary mucinous neoplasms, solid papillary tumors, lymphoepithelial cysts, cystic teratomas, and malignant tumors such as pancreatic ductal adenocarcinoma (PDAC), acinar cell carcinoma, and neuroendocrine tumors. In one example, the pancreatic cancer may include pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN), pancreatic mucinous cystic neoplasm (MCN), and neuroendocrine tumors of the pancreas. Pancreatic cancer diagnosable by the biomarkers provided in this specification may be selected from the pancreatic cancers described above, for example, from malignant tumors, but is not limited thereto.

[0055] Additionally, in this specification, "pancreatic cancer" may refer to early pancreatic cancer (e.g., resectable pancreatic cancer), late pancreatic cancer (e.g., borderline resectable pancreatic cancer, locally advanced pancreatic cancer, metastatic pancreatic cancer), or all of these, based on the stage of progression. Pancreatic cancer diagnosable by the diagnostic technology provided in this application is characterized by not being affected by the level of CA19-9. That is, pancreatic cancer diagnosable by the diagnostic technology provided in this application may include not only pancreatic cancer with high levels of CA19-9, but also cases where the level of CA19-9 is low. The above phrase “low CA19-9 level” may mean that the level of CA19-9 protein in blood (e.g., serum, plasma, or whole blood) (measured, e.g., by ELISA, etc.) is equal to or lower than the normal range (normal range or lower), and for example, the CA19-9 protein level is 100 U / mL or less, 95 U / mL or less, 90 U / mL or less, 85 U / mL or less, 80 U / mL or less, 75 U / mL or less, 70 U / mL or less, 65 U / mL or less, 60 U / mL or less, 55 U / mL or less, 50 U / mL or less, 45 U / mL or less, 40 U / mL or less, 39 U / mL or less, 38 U / mL or less, or 37 U / mL or less, for example, It may include, but is not limited to, pancreatic cancer with <37.0 U / mL (CA19-9 <37.0 U / mL).

[0056] In this specification, “diagnosis” may include, but is not limited to, determining the susceptibility of a subject to a specific disease or condition, determining whether the subject currently has a specific disease or condition, determining the risk of the subject contracting a specific disease or condition, determining the prognosis of a subject contracting a specific disease or condition (e.g., identification of a pre-metastatic or metastatic cancer state, determination of the stage of the cancer, determination of the responsiveness of the cancer to treatment, etc.), and / or therametrics (e.g., monitoring the condition of the subject to provide information on therapeutic efficacy).

[0057] In this specification, the diagnosis of pancreatic cancer may mean confirming whether the above-mentioned pancreatic cancer (e.g., early pancreatic cancer, late pancreatic cancer, or both) has occurred, the likelihood of occurrence (risk), and / or the degree of progression of the subject, and / or distinguishing it from normal or other similar diseases (e.g., benign pancreatic diseases (pancreatitis (chronic pancreatitis and / or acute pancreatitis), benign pancreatic tumors (e.g., intraductal papillary mucinous tumor (IPMN)), other pancreatic diseases such as autoimmune pancreatitis (AIP), etc.; pancreatic diseases excluding pancreatic cancer (pancreatic malignant tumor)).

[0059] Biomarkers for diagnosis of pancreatic cancer

[0060] In this specification, as a biomarker for the diagnosis of pancreatic cancer,

[0061] (1) CA19-9 and / or its coding gene, and

[0062] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19; e.g., (i) IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, (ii) SLC27A2, PTGES2, and their coding genes, and (ii) one or more selected from the group consisting of IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, PTGES, and their coding genes)

[0063] A combination of is provided. The biomarkers described herein may mean proteins and / or genes encoding them.

[0064] In one embodiment, the biomarker for diagnosing pancreatic cancer is,

[0065] (1) CA19_9 and / or its coding gene; and

[0066] (2) (i) one or more selected from the group consisting of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, and PTGES2, or their coding genes, or

[0067] (ii) one or more selected from the group consisting of IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, and PTGES, or their coding genes

[0068] It may include.

[0069] In another embodiment, the biomarker for diagnosing pancreatic cancer is,

[0070] (1) CA19_9 and / or its coding gene; and

[0071] (2) (a) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, SLC27A2, and PTGES2 or their coding genes; and

[0072] (b) (i) ARG1 or its coding gene, or

[0073] (ii) one or more selected from the group consisting of TNF, CXCL8, CXCR4, CCR5, and PTGES, or one or more coding genes said one or more

[0074] It may include.

[0075] More specifically, the biomarker for the diagnosis of pancreatic cancer mentioned above is,

[0076] (1) CA19_9 and / or its coding gene; and

[0077] (2) (i) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, and PTGES2 or their coding genes, or

[0078] (ii) IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, and PTGES, or combinations thereof, or genes coding for them

[0079] It may include.

[0080] The above biomarker may be used for the diagnosis of pancreatic cancer, e.g., early pancreatic cancer, late pancreatic cancer, or all pancreatic cancers regardless of stage (early pancreatic cancer and late pancreatic cancer). The pancreatic cancer may include, but is not limited to, pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN), pancreatic mucinous cystic neoplasm (MCN), neuroendocrine tumors of the pancreas, etc.

[0081] The above biomarkers may be of origin from mammals, such as humans, primates including monkeys, and rodents including mice and rats, but are not limited thereto.

[0082] Specific details of the above biomarkers are exemplified in Table 1 below:

[0083] marker representative (gene) protein Full Name / Synonym IFNL1(INFλ1) NM_172140 NP_742152 Interferon lambda 1 / Interleukin-29 (IL-29) IFNG NM_000619 NP_000610 Interferon gamma CXCL11 NM_005409 NP_005400 CXC motif chemokine ligand 11 / I-TAC, IP-9 TNF NM_000594 NP_000585 Tumor necrosis factor / TNF-alpha IFNA1 NM_024013 NP_076911 Interferon alpha 1 CLEC7A NM_022570 NP_072092 C-type lectin domain containing 7A / DECTIN1 CXCL8 NM_000584 NP_000575 CXC motif chemokine ligand 8 / IL8, NAF, GCP1 FOXP3 NM_014009 NP_054728 Forkhead box P3 VEGFA NM_003376 NP_003367 Vascular endothelial growth factor A / VEGF, MVCD1, VPF CCL2 NM_002982 NP_002973 CC motif chemokine ligand 2 / MCP1, SCYA2 CC5 NM_002985 NP_002976 CC motif chemokine ligand 5 / RANTES, SCYA5 CCR5 NM_000579 NP_000570 CC motif chemokine receptor 5 / CKR5, CD195, CCCKR5 CXCR4 NM_003467 NP_003458 CXC motif chemokine receptor 4 / LESTR, CD184, Fusin ARG1 NM_001244438 NP_001231367 Arginase 1 CXCR2 NM_001557 NP_001548 CXC motif chemokine receptor 2 / CD182, IL8R2, IL8RA PTGS2 NM_000963 NP_000954 Prostaglandin-endoperoxide synthase 2 / COX2, PHS-2 PTGES2 NM_198938 NP_945176 Prostaglandin E synthase 2 / GBF1, mPGES-2 SLC27A2 NM_003645 NP_003636 Solute carrier family 27 member 2 / FATP2, VLCS PTGES NM_004878 NP_004869 Prostaglandin E synthase / PGES, MPGES, mPGES-1

[0085] Detectable preparations of biomarkers

[0086] In this specification, detection of a biomarker may mean measuring the presence of a biomarker in a sample and / or the level (concentration) of the biomarker.

[0087] The above biomarkers are as described above.

[0088] In this specification, biomarkers, i.e.,

[0089] (1) CA19-9 and / or its coding gene, and

[0090] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19)

[0091] The agent capable of detecting it can be selected from all small molecule compounds, proteins, and nucleic acid molecules capable of binding to the biomarker.

[0092] In one example, where the biomarker is one or more proteins selected from the group consisting of (1) CA19-9, and (2) SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, and PTGES (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19), the detectable agent of the biomarker is a protein that binds to the protein (e.g., an antibody, an antigen-binding fragment of an antibody, It may be one or more selected from the group consisting of antibody analogs containing antigen-binding fragments of antibodies, receptors, etc.), peptides, nucleic acid molecules (e.g., polynucleotides, oligonucleotides, etc.), small molecule compounds (chemical, small molecule), etc., but is not limited thereto.

[0093] In another example, where the biomarker is a coding gene (whole-length DNA, cDNA, or mRNA) of one or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, and PTGES (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19), the detectable agent of the biomarker is a nucleic acid molecule (oligonucleotide) capable of binding (or hybridizing) to the gene, It may be one or more selected from the group consisting of polynucleotides, etc.; e.g., primers, probes, aptamers, antisense oligonucleotides, etc.), small molecule compounds, proteins, peptides, etc., but is not limited thereto.

[0094] The detectable formulation of the above biomarker may be labeled with or unlabeled with conventional labeling substances such as fluorescent substances, chromogenic substances, luminescent substances, radioisotopes, and heavy metals.

[0095] In one example, if the biomarker is a protein as described above, the detection of the biomarker may be performed through conventional protein detection (or measurement or analysis) methods such as conventional enzyme reactions, fluorescence, luminescence and / or radiation detection, and specifically, may be measured by a method selected from the group consisting of immunochromatography, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), enzyme immunoassay (EIA), fluorescence immunoassay (FIA), luminescence immunoassay (LIA), Western blotting, microarray method, flow cytometry, etc., but is not limited thereto.

[0096] In another example, if the biomarker is the gene described above, the detection of the biomarker may be performed using a conventional gene detection (or measurement or analysis) method. For example, it may be measured using a conventional gene analysis method that uses a primer, probe, aptamer, or antisense oligonucleotide capable of hybridizing with the gene, specifically, polymerase chain reaction (PCR; e.g., qPCR, real-time PCR, real-time qPCR, etc.), FISH (fluorescent in situ hybridization), Southern blotting, microarray method, etc., but is not limited thereto. In one embodiment, the primer may be a primer pair capable of detecting a gene fragment of 5 to 1000 bp, for example, 10 to 500 bp, 20 to 200 bp, or 50 to 200 bp, in the nucleotide sequence of the gene (whole-length DNA, cDNA, or mRNA), and may include a nucleotide sequence capable of hybridizing (e.g., complementary) with a nucleotide sequence of 5 to 100 bp, for example, 5 to 50 bp, 5 to 30 bp, or 10 to 25 bp, respectively, at the 3'-terminus and 5'-terminus of the gene fragment. The probe, aptamer, or antisense oligonucleotide may have a total length of 5 to 1000 bp, 5 to 500 bp, 5 to 200 bp, 5 to 100 bp, 5 to 50 bp, 5 to 30 bp, or 5 to 25 bp, and may have a base sequence capable of binding to or hybridizing (e.g., complementary) with a gene fragment of 5 to 1000 bp, 5 to 500 bp, 5 to 200 bp, 5 to 100 bp, 5 to 50 bp, 5 to 30 bp, or 5 to 25 bp in the base sequence of the marker gene (whole-length DNA, cDNA, or mRNA).The term "combinable" above may mean that it can be combined with all or part of the gene by chemical and / or physical bonding, such as covalent bonding, and the term "hybridizable" above may mean that it can be combined by having sequence complementarity of 80% or more, e.g., 90% or more, 95% or more, 98% or more, 99% or more, or 100% with the base sequence of the gene region.

[0098] Detection of biomarkers

[0099] In this specification, the “detection of a biomarker” described above may mean measuring the presence and / or level of a biomarker described in this specification.

[0100] When the above biomarker is a protein, the detection of the biomarker may be performed by a method selected from the group consisting of conventional protein detection methods, such as immunochromatography, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), enzyme immunoassay (EIA), fluorescence immunoassay (FIA), luminescence immunoassay (LIA), Western blotting, microarray method, flow cytometry method, etc., but is not limited thereto.

[0101] When the above biomarker is a gene, detection of the biomarker may be performed by a method selected from the group consisting of conventional gene detection methods, such as polymerase chain reaction (PCR; e.g., qPCR, real-time PCR, real-time qPCR, etc.), FISH (fluorescent in situ hybridization), Southern blotting, microarray methods, etc., but is not limited thereto.

[0102] In this specification, “measurement of biomarker levels” may be performed by qualitatively and / or quantitatively analyzing the results obtained by the biomarker (protein and / or gene) detection method described above.

[0103] In one example, when using a labeled detection agent, the level measurement of the biomarker may be performed by qualitatively analyzing the signal intensity obtained from the label using conventional methods (e.g., measuring the intensity (brightness) or area of ​​fluorescence and / or luminescence, the thickness and / or intensity of gel electrophoresis bands, etc., and / or comparing it with a predetermined reference sample (e.g., normal sample, etc.)), or by quantitatively analyzing using conventional methods (e.g., quantifying the concentration of protein and / or gene, the intensity (brightness) or area of ​​fluorescence and / or luminescence, the thickness and / or intensity of gel electrophoresis bands, etc.), but is not limited thereto.

[0104] In other examples, when using a conventional polymerase chain reaction method (PCR; e.g., qPCR, real-time PCR, real-time qPCR, etc.), the level measurement of the biomarker may be performed by conventional PCR quantification methods such as measuring the Ct (cycle threshold) value, ΔCt value (ΔCt = Ct (Gene) - Ct (Control gene)), and / or ΔΔCt value (ΔΔCt = ΔCt (Target gene) - ΔCt (Reference gene)), but is not limited thereto.

[0105] In another example, after the biomarker level measurement step, the method may further include a step of performing conventional statistical analysis on the biomarker levels quantified (numerical) as described above (e.g., in the case of genes, Ct value, ΔCt value, ΔΔCt value, 2^-dCt, and / or 2^-ddCt measured by PCR; in the case of proteins, concentration (U / mL) measured by ELISA, etc.). The term "statistical analysis" may be used to encompass all conventional statistical analysis methods, such as various machine learning models and / or algorithms. Examples of the above statistical analysis include regression analysis (e.g., logistic regression, stepwise logistic regression, etc.), Ensemble, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Light GBM (Light Gradient Boosting Machine), Gaussian Naive Bayes, SVM (Support Vector Machine), Bagging (Bootstrap Aggregating), Boosting, etc., but are not limited thereto. For instance, the above statistical analysis may be performed by regression analysis (e.g., logistic regression, stepwise logistic regression, etc.), and may be performed by additionally conducting appropriate statistical analysis (e.g., one or more selected from the statistical analysis methods described above) as needed.

[0106] In one embodiment of the present application, (1) concentration of CA19_9 (U / mL) in a blood sample (buffy coat or serum) (e.g., measured by ELISA); and (2) based on the ΔCt values ​​(e.g., measured by qPCR) of one or more coding genes selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, and PTGES (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19), logistic regression (LR), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), Analysis of biomarkers was performed using a machine learning model with a total of five classifiers, including support vector machine (SVM), but is not limited to this.

[0108] Composition and kit for diagnosing pancreatic cancer

[0109] The composition for diagnosing pancreatic cancer provided in this specification may include a detectable agent of the biomarker described above.

[0110] The pancreatic cancer diagnostic kit provided herein may comprise a detectable agent of the biomarker described above or a pancreatic cancer diagnostic composition containing the same, and said detection means. The said detection means may be a means capable of qualitatively and / or quantitatively analyzing the presence and / or level of the biomarker detected by the detectable agent of the biomarker. In one example, the said detection means may be selected from among means for use in the protein and / or gene detection method described above.

[0111] In one example, the kit may be an RT-PCR kit, a DNA chip kit, an ELISA kit, a protein chip kit, a rapid kit, or an MRM (Multiple reaction monitoring) kit, but is not limited thereto.

[0112] In one embodiment, the pancreatic cancer diagnostic composition and / or kit is,

[0113] (1) CA19-9, and

[0114] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19)

[0115] It may include a detectable agent.

[0117] Pancreatic cancer diagnosis methods

[0118] The method for diagnosing pancreatic cancer (or the method for providing information for diagnosis) provided in this specification may include the step of detecting the biomarker described above in a blood sample separated from a target.

[0119] In the above method, the step of detecting the biomarker may be a step of confirming the presence of the biomarker in the sample, measuring the level (concentration) of the biomarker, or performing both. The diagnosis of the pancreatic cancer patient may be to confirm or determine that the sample subject to biomarker measurement or the patient from whom the sample originated is a pancreatic cancer patient, and / or to distinguish the pancreatic cancer patient from patients with similar diseases. Examples of the pancreatic cancer may include pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN), pancreatic mucinous cystic neoplasm (MCN), neuroendocrine tumor of the pancreas, etc. The above pancreatic cancer may mean early pancreatic cancer, late pancreatic cancer, or both; the above normal individual may mean an individual that does not have pancreatic cancer (early pancreatic cancer, late pancreatic cancer, or both); and the above similar disease may mean other pancreatic diseases such as pancreatitis (chronic pancreatitis and / or acute pancreatitis), pancreatic benign tumors (e.g., intraductal papillary mucinous tumor (IPMN)), autoimmune pancreatitis (AIP) (pancreatic diseases excluding pancreatic cancer (pancreatic malignant tumor)), etc.

[0121] In one example, the method for diagnosing pancreatic cancer or the method for providing information for diagnosing pancreatic cancer provided in this specification is,

[0122] (a) a step of detecting the previously described biomarker in a blood sample separated from the subject, and

[0123] (b) A step of analyzing the biomarker detection results obtained in step (a) above

[0124] It may include.

[0125] The biomarker detection in step (a) above may be a measurement of the biomarker level, and the specific method for measuring the biomarker level is as described above.

[0126] The analyzing step of step (b) above may be performed by performing conventional statistical analysis on the quantified (numerical) biomarker levels as previously described (e.g., in the case of genes, Ct values, ΔCt values, ΔΔCt values, 2^-dCt, and / or 2^-ddCt measured by PCR; in the case of proteins, concentrations (U / mL) measured by ELISA, etc.). The term "statistical analysis" may be used to encompass all conventional statistical analysis methods, such as various machine learning models and / or algorithms. Examples of the above statistical analysis include regression analysis (e.g., logistic regression, stepwise logistic regression, etc.), Ensemble, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Light GBM (Light Gradient Boosting Machine), Gaussian Naive Bayes, SVM (Support Vector Machine), Bagging (Bootstrap Aggregating), Boosting, etc., but are not limited thereto. For instance, the above statistical analysis may be performed by regression analysis (e.g., logistic regression, stepwise logistic regression, etc.), and may be performed by additionally conducting appropriate statistical analysis (e.g., one or more selected from the statistical analysis methods described above) as needed.

[0127] In one embodiment of the present application, (1) concentration (U / mL) of CA19_9 protein in a blood sample (buffy coat or serum) (e.g., measured by ELISA); and (2) based on the ΔCt values ​​(e.g., measured by qPCR) of one or more coding genes selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, and PTGES (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19), logistic regression (LR), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), Analysis of biomarkers was performed using a machine learning model with a total of five classifiers, including support vector machine (SVM), but is not limited to this.

[0128] When performing conventional statistical analysis (e.g., machine learning), the analysis result is converted into a value between 0 and 1, and based on a unique cut-off value determined according to the statistical analysis (machine learning) model used and / or the combination of biomarkers used, the converted value can be determined as positive (pancreatic cancer) if it is greater than or equal to the cut-off value, and as negative (normal) if it is less than or equal to the cut-off value. The cut-off value is a numeric value selected between 0 and 1, which can be determined by a person skilled in the art according to the statistical analysis (machine learning) model and / or the combination of biomarkers used.

[0129] The above method, after step (b),

[0130] (c) a step of comparing the analysis result value obtained in step (b) with a cut-off value determined according to the analysis model, and if the analysis result value is greater than or equal to the cut-off value, diagnosing (predicting, determining, judging, confirming) the subject from which the blood sample was isolated as a pancreatic cancer patient; and / or if the analysis result value is less than the cut-off value, determining (judging, confirming) that the subject from which the blood sample was isolated is not a pancreatic cancer patient (e.g., a normal or similar disease patient).

[0131] It may additionally include.

[0133] The method for diagnosing pancreatic cancer or the method for providing information for diagnosing pancreatic cancer provided in this specification, after the step of detecting, the step of analyzing, or the step of diagnosing (predicting, judging, confirming, or determining),

[0134] The stage of performing pancreatic cancer treatment on a patient diagnosed with pancreatic cancer.

[0135] It may additionally include.

[0136] The above pancreatic cancer treatment may mean chemotherapy such as the administration of pancreatic cancer treatments (e.g., anticancer agents: 5-fluorouracil, gemcitabine, tarceva (erlotinib), antibodies, etc.), radiation therapy, surgical procedures, or a combination of two or more of these.

[0137] The above treatment for pancreatic cancer may be applied differently depending on the stage and progression of the pancreatic cancer. Early-stage pancreatic cancer (e.g., resectable pancreatic cancer, etc.) is pancreatic cancer at a stage that can be treated by surgical removal, and treatment for early-stage pancreatic cancer may involve performing only surgical removal or performing adjuvant chemotherapy following surgical removal. Adjuvant chemotherapy refers to anticancer therapy other than surgical removal and may include chemotherapy such as the administration of pancreatic cancer treatments (e.g., one or more anticancer agents selected from the group consisting of 5-fluorouracil, gemcitabine, tarceva (erlotinib), leucovorin, irinotecan, oxaliplatin, antibodies, etc.), anti-radiation therapy, or a combination thereof.

[0138] Treatment for late-stage pancreatic cancer (e.g., borderline resectable pancreatic cancer, locally advanced pancreatic cancer, metastatic pancreatic cancer, etc.) can be applied differently depending on the stage of progression of each type of cancer.

[0139] For example, in the case of borderline resectable pancreatic cancer among late-stage pancreatic cancers, after performing adjuvant therapy (e.g., administration of one or more anticancer drugs selected from a group consisting of 5-fluorouracil, leucovorin, irinotecan, oxaliplatin, etc. (e.g., FOLFIRINOX, which is a combination of the four anticancer drugs)), depending on the course of treatment, i) postoperative chemotherapy may be performed if surgical removal is possible, and ii) chemotherapy may be performed if surgical removal is not possible. The above anticancer treatment may mean chemotherapy such as the administration of a pancreatic cancer treatment drug (e.g., one or more anticancer agents selected from the group consisting of 5-fluorouracil, gemcitabine, tarceva (erlotinib), leucovorin, irinotecan, oxaliplatin, antibodies, etc.), anti-radiation therapy, or a combination thereof.

[0140] In the case of locally advanced pancreatic cancer among late-stage pancreatic cancers, after performing neoadjuvant therapy (e.g., administration of one or more anticancer agents selected from the group consisting of 5-fluorouracil, leucovorin, irinotecan, oxaliplatin, etc. (e.g., FOLFIRINOX, which is administered by mixing the four anticancer agents)), depending on the progression, the neoadjuvant therapy described above may be continued or secondary therapy may be performed (e.g., administration of a combination of nanoparticle albumin-paclitaxel and gemcitabine), anti-radiation therapy, or a combination of two or more of these.

[0141] In the case of metastatic pancreatic cancer among late-stage pancreatic cancers, secondary treatment may be performed depending on the patient's course after performing neoadjuvant therapy (e.g., administration of one or more anticancer agents selected from the group consisting of 5-fluorouracil, leucovorin, irinotecan, oxaliplatin, etc. (e.g., FOLFIRINOX, which treats with a combination of the above four anticancer agents), anticancer therapy (e.g., administration of a combination of nanoparticle albumin-paclitaxel (nab-paclitaxel) and gemcitabine, or administration of gemcitabine alone, etc.), or a combination thereof. The above secondary treatment may be, for example, administration of a combination of nanoparticle albumin-paclitaxel (nab-paclitaxel) and gemcitabine, or It may refer to chemotherapy including the administration of a mixture of 5-fluorouracil and liposomal irinotecan, or a mixture of 5-fluorouracil and liposomal oxaliplatin.

[0143] blood sample

[0144] The applicable diagnostic target sample or blood sample of the composition, kit, and method for diagnosing pancreatic cancer provided in this specification may comprise a liquid biopsy obtained (or separated or derived) from the diagnostic target, e.g., blood (whole blood), serum, plasma, and / or cells separated therefrom, and in one example, the sample may comprise a buffy coat separated from the diagnostic target.

[0145] In this specification, a buffy coat refers to the entire white blood cell layer formed between the upper plasma layer and the lower red blood cell layer during the centrifugation of blood; it is a mixture of components of blood excluding plasma and red blood cells (e.g., monocytes, granulocytes, lymphocytes, etc.) and is used in a sense clearly distinguished from peripheral blood mononuclear cells (PBMCs) (see FIG. 3). The blood sample used in this specification may be a blood-derived buffy coat and may not be a sample composed solely of peripheral blood mononuclear cells. In one example, the buffy coat may be obtained from the middle buffy coat layer among the plasma layer (top), buffy coat layer (middle), and red blood cell layer (bottom) obtained sequentially from top by centrifuging blood under one or more conditions selected from the following conditions (1) to (4) (1, 2, 3, or 4):

[0146] (1) Temperature: 2 to 30°C, 2 to 28°C, 2 to 25°C, 2 to 23°C, 2 to 20°C, 2 to 18°C, 2 to 15°C, 2 to 13°C, 2 to 10°C, 2 to 8°C, 2 to 6°C, 2 to 5°C, 2 to 4°C, 3 to 30°C, 3 to 28°C, 3 to 25°C, 3 to 23°C, 3 to 20°C, 3 to 18°C, 3 to 15°C, 3 to 13°C, 3 to 10°C, 3 to 8°C, 3 to 6°C, 3 to 5°C, 3 to 4°C, 4 to 30°C, 4 to 28°C, 4 to 25°C, 4 to 23°C, 4 to 20°C, 4 to 18°C, 4 to 15°C, 4 to 13°C, 4 to 10°C, 4 to 8°C, 4 to 6°C, 4 to 5°C, 5 to 30°C, 5 to 28°C, 5 to 25°C, 5 to 23°C, 5 to 20°C, 5 to 18°C, 5 to 15°C, 5 to 13°C, 5 to 10°C, 5 to 8°C, 5 to 6°C, 10 to 30°C, 10 to 28°C, 10 to 25°C, 10 to 23°C, 10 to 20°C, 10 to 18°C, 10 to 15°C, 10 to 13°C, 15 to 30°C, 15 to 28°C, 15 to 25°C, 15 to 23℃, 15 to 20℃, 15 to 18℃, 20 to 30℃, 20 to 28℃, 20 to 25℃, or 20 to 23℃;

[0147] (2) Speed: 300g to 2000g, 300g to 1800g, 300g to 1500g, 300g to 1300g, 300g to 1000g, 500g to 2000g, 500g to 1800g, 500g to 1500g, 500g to 1300g, 500g to 1000g, 600g to 1000g, 700g to 1000g, 800g to 1000g, 500g to 900g, 600g to 900g, 700g to 900g, 800g to 900g, 500g to 800g, 600g to 800g, or 700g to 800g;

[0148] (3) Time: 5 to 20 minutes, 7 to 20 minutes, 9 to 20 minutes, 5 to 15 minutes, 7 to 15 minutes, 9 to 15 minutes, 5 to 12 minutes, 7 to 12 minutes, or 9 to 12 minutes; and

[0149] (4) No addition of density gradient medium.

[0150] In this specification, the subject of diagnosis (individual) may be selected from mammals including humans, primates including monkeys, mice, rodents including rats, etc., and may be an individual requiring diagnosis of pancreatic cancer.

[0152] Diagnosis of bile duct cancer

[0153] The diagnostic technology provided in this application can also be applied to the diagnosis of bile duct cancer.

[0154] Accordingly, another example is,

[0155] (1) CA19-9, and

[0156] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0157] The present invention provides a biomarker for the diagnosis of bile duct cancer, comprising: a buffy coat, etc. The biomarker may be for detection or analysis in blood samples, e.g., whole blood, plasma, serum and / or buffy coat.

[0158] Another example is,

[0159] (1) CA19-9, and

[0160] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0161] The present invention provides a composition for diagnosing bile duct cancer or a kit for diagnosing bile duct cancer comprising a preparation capable of detecting [the substance]. The composition for diagnosing bile duct cancer or the kit for diagnosing bile duct cancer may be intended for use in blood samples, e.g., whole blood, plasma, serum, and / or buffy coat.

[0162] Another example is, in a blood sample separated from a subject,

[0163] (1) CA19-9, and

[0164] (2) One or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes.

[0165] A method for diagnosing bile duct cancer or a method for providing information for the diagnosis of bile duct cancer is provided, comprising a step of detecting.

[0166] More specifically, the method for diagnosing bile duct cancer or the method for providing information for the diagnosis of bile duct cancer provided in this specification is,

[0167] (a) a step of detecting the previously described biomarker in a blood sample separated from the subject, and

[0168] (b) A step of analyzing the biomarker detection results obtained in step (a) above

[0169] It may include, and optionally,

[0170] (c) A step of comparing the analysis result value obtained in step (b) with a cut-off value determined according to the analysis model, and if the analysis result value is greater than or equal to the cut-off value, diagnosing (predicting, determining, judging, confirming) the subject from which the blood sample was isolated as a bile duct cancer patient; and / or if the analysis result value is less than the cut-off value, determining (judging, confirming) that the subject from which the blood sample was isolated is not a bile duct cancer patient (e.g., normal or a patient with a similar disease).

[0171] It may additionally include.

[0172] The above method for diagnosing bile duct cancer or the method for providing information for diagnosing bile duct cancer is, after the above detection step, analysis step, or diagnosis (prediction, judgment, confirmation, or determination) step,

[0173] Step of performing bile duct cancer treatment on a patient diagnosed with bile duct cancer

[0174] It may additionally include.

[0175] The above treatment for bile duct cancer may mean chemotherapy such as the administration of a treatment for bile duct cancer (e.g., anticancer drugs, antibodies, etc.), radiation therapy, surgical procedures, or a combination of two or more of these.

[0176] The blood sample may include whole blood, plasma, serum, and / or buffy coat. The detecting step may include measuring the presence and / or level (e.g., concentration, expression level, etc.) of the biomarker (protein and / or gene (DNA, mRNA, etc.) described above in the blood sample.

[0177] Specific details of the above composition, kit, and method may be referenced from the contents described in the previously explained diagnosis of pancreatic cancer.

[0179] All figures provided in this specification are to be interpreted as including a conventional margin of error to the extent that the desired function and / or effect of the present application can be achieved, for example, to include a range of -10% (or -9%, -8%, -7%, -6%, -5%, -4%, -3%, -2%, or -1%) to +10% (or +9%, +8%, +7%, +6%, +5%, +4%, +3%, +2%, or +1%), but are not limited thereto. Effects of the invention

[0181] Through the present application, pancreatic cancer can be diagnosed non-invasively, simply, and with high accuracy using only patient-derived blood, and can be usefully utilized to more accurately diagnose the stage of progression of pancreatic cancer and establish more effective treatment strategies for pancreatic cancer. Brief explanation of the drawing

[0183] Figure 1 schematically shows the classification according to A of Example 3. Figure 2 shows the performance evaluation results according to C of Example 3. Figures 3 to 6 are graphs showing the diagnostic performance of pancreatic cancer (PDAC) using biomarker model 1 (IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, PTGES2 (11 markers in total) + CA19_9) compared to when CA19_9 is used alone. Figures 7 to 10 are graphs showing the diagnostic performance of pancreatic cancer (PDAC) using biomarker model 2 (IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, PTGES (15 markers) + CA19_9) compared to when CA19_9 is used alone. Specific details for implementing the invention

[0184] The present invention will be explained in more detail below through examples and test examples. However, these examples and test examples are intended to illustrate the present invention and should not be interpreted as limiting the present invention.

[0186] Example 1: Preparation of Sample

[0187] To confirm the expression patterns of pancreatic cancer biomarkers in pancreatic cancer, mRNA expression levels of various markers and CA19-9 protein expression levels were measured in blood-derived buffy coat and serum from pancreatic cancer patients. In addition, for comparison, the same test was performed on blood-derived buffy coat and serum from normal individuals without pancreatic cancer (normal control group).

[0189] Example 1-1: buffy coat

[0190] 8 ml of blood was collected from each of 417 patients with pancreatic ductal adenocarcinoma (PDAC) (76 with early-stage pancreatic cancer, 341 with late-stage pancreatic cancer) and 1087 healthy individuals and placed in EDTA tubes. Within 2 hours of collection, the EDTA tubes were centrifuged at 1800 g at 24°C for 10 minutes. From the blood separated in the order of plasma, buffy coat, and red blood cells from the supernatant, the plasma from the supernatant was removed, and 250 μl of the buffy coat was separated into a storage tube and stored in a -80°C deep freezer.

[0192] Examples 1-2: Serum

[0193] Whole blood samples for CA19-9 measurement were collected in standard sample collection tubes or serum separating tubes (SSTs) from 417 pancreatic cancer patients (early pancreatic cancer: 76, late pancreatic cancer: 341) and 1087 healthy individuals. The samples were then centrifuged at 1800 g, 10 minutes, and 24 °C to separate only the serum layer, which was dispensed into storage tubes and stored under refrigeration (2–8 °C) or in an ultra-low temperature freezer (-80 °C).

[0194] The above pancreatic cancer patients were classified into early-stage and late-stage pancreatic cancer patients based on the criteria in Table 2 below:

[0195] stage criteria Classification during statistical analysis RPC Resectable Pancreatic Cancer Early Pancreatic Cancer (Early PC) BRPC Borderline Resectable Pancreatic Cancer Late Pancreatic Cancer (Late PC) LAPC Locally advanced Pancreatic Cancer Late Pancreatic Cancer (Late PC) MPC Metastatic Pancreatic Cancer Late Pancreatic Cancer (Late PC)

[0197] Example 2: Measurement of expression level

[0198] 2-1: RT-qPCR

[0199] Gene expression in the sample prepared in Example 1 above was measured by Real-time qPCR, and the Real-time qPCR was performed using probe-based multiplex PCR.

[0200] First, total RNA was extracted from 250 μl of the frozen buffy coat from Example 1 using the NucleoSpin® RNA Blood kit (MACHEREY-NAGEL) according to the manufacturer's recommended protocol. cDNA was synthesized using 1 μg of RNA per sample. cDNA synthesis was performed using the GoScript™ Reverse Transcription system kit (Promega).

[0201] For Multiplex PCR, probes for each marker were labeled with FAM dye, and GAPDH, used as the internal reference gene, was labeled with HEX dye. Primers and probes were purchased from IDT (Integrated DNA Technologies, Inc.).

[0202] To verify the expression of each gene in the samples, preparations were made to a final volume of 20 μl according to the protocol of GoTaq® Probe qPCR Master Mix (Promega). Gene expression tests were performed using a QuantStudio 3, 5 Real-Time PCR system (Applied Biosystems) under Standard Cycling conditions provided by the instrument's software. Based on the Real-time qPCR results, the Ct values ​​representing the mRNA expression levels of each marker and the GAPDH mRNA expression levels were extracted, and the ΔCt values ​​were obtained by calculating the difference between the mRNA expression levels of each marker and the GAPDH mRNA expression levels.

[0203] The markers used are shown in Table 3, and the nucleic acid sequences (5'→3') of the primers and probes for each marker are shown in Table 4 below:

[0204] No. Gene name Ref. sequence (NCBI) IDT Assay Name 1 IL29(IFNL1) NM_172140 Hs.PT.56a.21113836.g 2 IFNG NM_000619 Hs.PT.58.3781960 3 CXCL11(I-TAC) NM_005409 Hs.PT.58.26723814 4 TNF NM_000594 Hs.PT.58.45380900 5 IFNA1 NM_024013 Hs.PT.58.46311748.g 6 DECTIN-1(CLEC7A) NM_022570(5) Hs.PT.58.40018774 7 IL-8(CXCL8) NM_000584 Hs.PT.58.38869678.G 8 FOXP3 NM_014009 Hs.PT.58.3671186 9 VEGFA NM_003376 Hs.PT.58.1149801 10 CCL2 NM_002982 Hs.PT.58.45467977 11 CCL5 NM_002985 Hs.PT.58.1724551 12 CCR5 NM_000579 Hs.PT.58.2249633 13 CXCR4 NM_003467 Hs.PT.58.22298491 14 ARG1 NM_001244438 Hs.PT.56a.20779559 15 CXCR2 NM_001557 Hs.PT.58.40527202 16 SLC27A2(FATP2) NM_003645 Hs.PT.58.38450787 17 PTGS2(COX2) NM_000963 Hs.PT.58.77266 18 PTGES2 NM_198938 Hs.PT.58.40424722 19 PTGES(PTGES1) NM_004878 Hs.PT.58.39853040

[0205] Marker Sequence IL-29 (IFNL1) Primer-F 5'-GGTTCAAATCTCTGTCACCACA-3' Primer-R 5'-GAAGACAGGAGAGCTGCAAC-3' Probe 5'-FAM / TCAAGAAGG / ZEN / CCAGGGACGCC / IBFQ-3' IFNG Primer-F 5'-gcaa caa aaa gaaa cga gat gac-3' Primer-R 5'-cga cag ttc agc cat cac tt-3' Probe 5'- / 56-FAM / tcg gta act / ZEN / gac ttg aat gtc caa cgc / 3IABkFQ / -3' CXCL11 (I-TAC) Primer-F 5'-tat tgt gtg cta cag ttg ttc aag-3' Primer-R 5'-ggt aca tta tgg agg ctt tct ca-3' Probe 5'- / 56-FAM / tgc cac ttt / ZEN / cac tgc ttt tac ccc a / 3IABkFQ / -3' TNF Primer-F 5'-tgcactttggagtgatcgg-3' Primer-R 5'-tcagcttgagggtttgctac-3' Probe 5'- / 56-FAM / agatgatct / ZEN / gactgcctgggcc / 3IABkFQ / -3' IFNA1 Primer-F 5'-ggaggttgtcagagcaga-3' Primer-R 5'-aatgacagaattcatgaaagcgt-3' Probe 5'- / 56-FAM / tgagatccc / ZEN / tctctttatcaacaaacttgcaa / 3IABkFQ / -3' DECTIN1 (CLEC7A) Primer-F 5-GAC TCT CAA AGC AAT ACC AGG A-3' Primer-R 5'-CCA CAG CTA TCA CCA GTA TTA CC-3' Probe 5'- / 56-FAM / TGC AGC ACA / ZEN / CGA TCC TTT CTC TGA A / 3IABkFQ / -3' IL-8 (CXCL8) Primer-F 5'-TGT CTG GAC CCC AAG GAA-3' Primer-R 5'-CAT CTT CAC TGA TTC TTG GAT ACC-3' Probe 5'-56-FAM / ACT TCT CCA / ZEN / CAA CCC TCT GCA CC / 3IABkFQ / -3' FOXP3 Primer-F 5'-CTA CTT CAA GTT CCA CAA CAT GC-3' Primer-R 5'-CCA GTG GTA GAT CTC ATT GAG TG-3' Probe 5'- / 56-FAM / CCT TTC ACC / ZEN / TAC GCC ACG CTC AT / 3IABkFQ / -3' VEGFA Primer-F 5'-GTA CAA GAT CCG CAG ACG TG-3' Primer-R 5'-TTC TGT ATC AGT CTT TCC TGG TG-3' Probe 5'-56-FAM / TCG TTT AAC / ZEN / TCA AGC TGC CTC GCC / 3IABkFQ / -3' CCL2 Primer-F 5'-AGC AGC CAC CTT CAT TCC-3' Primer-R 5'-GCC TCT GCA CTG AGA TCT TC-3' Probe 5'- / 56-FAM / TCA ATG CCC / ZEN / CAG TCA CCT GCT / 3IABkFQ / -3' CCL5 Primer-F 5'-GCT GTC ATC CTC ATT GCT ACT-3' Primer-R 5'-TGC CAC TGG TGT AGA AAT ACT C-3' Probe 5'- / 56-FAM / ATC TGC CTC / ZEN / CCC ATA TTC CTC GGA / 3IABkFQ / -3' CCR5 Primer-F 5'-GTC TAT ATG ATT GAT TTG CAC AGC TC-3' Primer-R 5'-TCA TAG ATT GGA CTT GAC ACT TGA-3' Probe 5'- / 56-FAM / CGT TCC CCT / ZEN / ACA AGA AAC TCT CCC C / 3IABkFQ / -3' CXCR4 Primer-F 5'-AGC AGG TAG CAA AGT GAC G-3' Primer-R 5'-CCT CGG TGT AGT TAT CTG AAG TG-3' Probe 5'- / 56-FAM / AGA TGC GGT / ZEN / GGC TAC TGG AGC / 3IABkFQ / -3' ARG1 Primer-F 5'-ACT CCA CTG ACA ACC ACA AG-3' Primer-R 5'-TGG CAG ATA TAC AGG GAG TCA-3' Probe 5'- / 56-FAM / CCT TCA GGA / ZEN / GGA AAG ATA CAG GTT GTC CA / 3IABkFQ / -3' CXCR2 Primer-F 5'-TCT GAC TAC CAC CCA ACC T-3' Primer-R 5'-CTG GGC TTT TCA CCT GTA GG-3' Probe 5'- / 56-FAM / CCA CTC CAA / ZEN / TAA CAG CAG GTC ACA GC / 3IABkFQ / -3' FATP2 (SLC27A2) Primer-F 5'-CTG TCA TTC AGT ATA TCG GTG AAC T-3' Primer-R 5'-ATC TCC TCG TAA GCC ATT TCC-3' Probe 5'- / 56-FAM / TGC AAC TCA / ZEN / CCA CAG AAA CCA AAT GAC / 3IABkFQ / -3' COX2 (PTGS2) Primer-F 5'-GCA CTA CAT ACT TAC CCA CTT CA-3' Primer-R 5'-GCC ATA GTC AGC ATT GTA AGT TG-3' Probe 5'- / 56-FAM / ACA TCC AGA / ZEN / TCA CAT TTG ATT GAC AGT CCA / 3IABkFQ / -3' PTGES2 Primer-F 5'-GCA ACA ACT AAA TGA CTC CTC TG-3' Primer-R 5'-CTG GGT AGT AGG TGA TGA TCT CT-3' Probe 5'- / 56-FAM / CCC GAC ACC / ZEN / AGG TAG GTC TTG AG / 3IABkFQ / -3' PTGES (PTGES1) Primer-F 5'-GTG GAA CGC TGC CTC AG-3' Primer-R 5'-CCA GAA AGG AGT AGA CGA AGC-3' Probe 5'- / 56-FAM / AGG AAG GGG / ZEN / TAG ATG GTC TCC ATG T / 3IABkFQ / -3' GAPDH (Reference gene) Primer-F 5'-ACATCGCTCAGACACCATG-3' Primer-R 5'-TGTAGTTGAGGTCAATGAAGGG-3' Probe 5'-HEX / AAG GTC GGA / ZEN / GTC AAC GGA TTT GGT C / IBFQ-3'

[0206] (In Table 4 above, the probe is a structure comprising a 5' FAM dye, an internal ZEN Quencher, and a 3' Iowa Black® Fluorescent Quencher (IBFQ).)

[0208] Data from samples with a GAPDH Ct value of 26.5 or higher in the qPCR experiment were excluded. All data used for statistical analysis were analyzed by calculating the ΔCt value. Feature selection was performed using logistic regression (LR), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), and support vector machine (SVM) classifiers to select marker combinations, and ROC curve analysis (statistical software: MedCalc) was performed to analyze AUC, sensitivity, and specificity.

[0210] 2-2 ELISA

[0211] CA19-9 was quantitatively analyzed using electrochemiluminescent immunoassay (ECLIA) with a measurement unit of U / mL. The Elecsys CA19-9 kit (manufacturer: Roche) and the cobas e 801 module (manufacturer: Roche) were used, and the manufacturer is Roche Diagnostics GmbH, Sandhofer Strasse 116, 68305 Manheim, Germany.

[0213] Example 3: Analysis of the relative importance of markers

[0214] A total of five classifiers were used in the development of the pancreatic cancer prediction model: logistic regression (LR), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), and support vector machine (SVM). For the machine learning algorithms, ΔCt values ​​for 19 candidate markers and the protein expression level of CA19-9 (U / ml) were provided as variables (features), and individual data points received a result value of 0 (negative) or 1 (positive) based on clinical information (pancreatic cancer positive or negative). The development of the machine learning model consisted of feature selection and performance evaluation. Python (version 3.11.4) was used for machine learning, while pandas and numpy were used for data processing, and sklearn was used for modeling and evaluation.

[0216] A. Train / Validation / Test Distribution

[0217] 5-fold cross-validation was used to balance predictive fitting and overfitting, and the total sample size was divided into a training set, validation set, and test set in a ratio of 64:16:20. For CA19-9 (protein) levels, which are commonly used for predicting pancreatic cancer, risk is assessed based on a threshold of 37.0 U / ml. Accordingly, the training, validation, and test sets were classified considering the disease and CA19-9 levels (see Figure 1).

[0219] B. Feature Selection

[0220] Feature selection for machine learning model development was performed for each classifier. The data used consist of training and validation sets, and the process proceeds through the following steps: 1) First, all possible marker panels composed of 19 markers are analyzed for a single classifier. During this process, 5-fold cross-validation (CV) is performed to calculate the average AUC value across five validation sets. 2) Hyperparameter tuning via grid search is conducted on the top 20 marker panels based on the 5-fold CV average AUC values ​​from the analysis. 3) During hyperparameter tuning, each of the top 20 marker panels is cross-paired with the remaining four classifiers in addition to the classifier selected in step 1, thereby filtering out a total of 500 hyperparameter-tuned marker panel + classifier combinations.

[0222] C. Performance Evaluation

[0223] For the machine learning model with completed feature selection, the combined training and validation sets were used for training, and the model was validated using an independent test set. During this process, parameters such as AUC, sensitivity, and specificity were calculated. The test set AUC values ​​of the 500 marker panels selected during feature selection were compared with the test set AUC values ​​of CA19-9 alone. The tests were conducted for four groups: Normal vs. PDAC, Normal vs. RPC, Normal vs. PDAC (CA19-9 < 37.0 U / ml), and Normal vs. RPC (CA19-9 < 37.0 U / ml). AUC was calculated using a Python program.

[0224] The ROC curve and AUC values ​​were calculated using probability values ​​derived from a machine learning model based on the test set. The AUC value is highest when CA19-9 is added to the marker panel (see Fig. 2).

[0226] More 1:

[0227] classifier: Randomforest

[0228] 마커: IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, PTGES2 (이상 11 markers) + CA19_9

[0230] (1) Analysis sample group: Normal vs PDAC (See Table 5 and Figure 3)

[0231]

[0233] (2) Analysis sample group: Normal vs RPC (See Table 6 and Figure 4)

[0234]

[0236] (3) Analysis sample group: Normal vs PDAC (CA19-9 (protein) < 37.0 U / ml) (See Table 7 and Figure 5)

[0237]

[0239] (4) Analysis sample group: Normal vs RPC (CA19-9 < 37.0 U / ml) (See Table 8 and Figure 6)

[0240]

[0242] more 2

[0243] classifier: Support Vector Machine

[0244] 마커: IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, PTGES (이상, 15 markers) + CA19_9

[0246] (1) Analysis sample group: Normal vs PDAC (See Table 9 and Figure 7)

[0247]

[0249] (2) Analysis sample group: Normal vs RPC (See Table 10 and Figure 8)

[0250]

[0252] (3) Analysis sample group: Normal vs PDAC (CA19-9 < 37.0 U / ml) (See Table 11 and Figure 9)

[0253]

[0255] (4) Analysis Sample Group: Normal vs RPC (CA19-9 < 37.0 U / ml) (See Table 12 and Figure 10)

[0256]

[0258] As examined above, the two models exemplified in the examples (IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, PTGES2 (11 markers) + CA19_9; and IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, PTGES (15 markers) + CA19_9) showed significantly superior pancreatic cancer diagnostic efficiency compared to the use of CA19_9 alone, and in particular, it was confirmed that pancreatic cancer can be diagnosed with excellent efficiency even when it is difficult to diagnose pancreatic cancer with CA19-9 because the CA19-9 (protein) level is less than 37.0 U / ml.

Claims

Claim 1 A composition for diagnosing pancreatic cancer comprising a detectable preparation of a biomarker including the following: (1) CA19-9 or its coding gene, and (2) one or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes. Claim 2 A composition for diagnosing pancreatic cancer according to claim 1, wherein the biomarker comprises: (1) CA19-9 or its coding gene; and (2) (a) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, SLC27A2, and PTGES2 or their coding genes; and (b) (i) ARG1 or its coding gene; or (ii) one or more selected from the group consisting of TNF, CXCL8, CXCR4, CCR5, and PTGES, or said one or more coding genes. Claim 3 A composition for diagnosing pancreatic cancer according to claim 2, wherein the biomarker comprises (1) CA19-9 or its coding gene, and (2) (i) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, and PTGES2 or their coding genes, or (ii) a combination of IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, and PTGES, or their coding genes. Claim 4 A composition for diagnosing pancreatic cancer according to any one of claims 1 to 3, wherein the pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN), pancreatic mucinous cystic neoplasm (MCN), or a neuroendocrine tumor of the pancreas. Claim 5 A composition for diagnosing pancreatic cancer according to any one of claims 1 to 3, wherein the pancreatic cancer has a blood CA19-9 protein level of less than 100 U / mL. Claim 6 A method for providing information for the diagnosis of pancreatic cancer, comprising the step of detecting a biomarker including the following in a blood sample isolated from a subject: (1) CA19-9 or its coding gene, and (2) one or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes. Claim 7 A method for providing information for the diagnosis of pancreatic cancer according to claim 6, wherein the biomarker comprises: (1) CA19-9 or its coding gene; and (2) (i) (a) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, SLC27A2, and PTGES2 or their coding genes; and (b) (i) ARG1 or its coding gene, or (ii) one or more selected from the group consisting of TNF, CXCL8, CXCR4, CCR5, and PTGES, or said one or more coding genes. Claim 8 A method for providing information for the diagnosis of pancreatic cancer according to claim 7, wherein the biomarker comprises (1) CA19-9 or its coding gene, and (2) (i) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, and PTGES2 or their coding genes, or (ii) a combination of IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, and PTGES, or their coding genes. Claim 9 A method for providing information on the diagnosis of pancreatic cancer according to any one of claims 6 to 8, wherein the pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN), pancreatic mucinous cystic neoplasm (MCN), or a neuroendocrine tumor of the pancreas. Claim 10 A method for providing information on the diagnosis of pancreatic cancer according to any one of claims 6 to 8, wherein the pancreatic cancer is one in which the level of CA19-9 protein in the blood is less than 100 U / mL. Claim 11 A composition for improving the efficacy of pancreatic cancer diagnosis using CA19-9, comprising a detectable preparation of a biomarker including the following: (1) CA19-9 or its coding gene, and (2) one or more selected from the group consisting of SLC27A2, IFNL1, IFNG, CXCL11, TNF, IFNA1, CLEC7A, CXCL8, FOXP3, VEGFA, CCL2, CCL5, CCR5, CXCR4, ARG1, CXCR2, PTGS2, PTGES2, PTGES, and their coding genes. Claim 12 A composition for improving the efficacy of diagnosing pancreatic cancer using CA19-9, wherein the biomarker comprises: (1) CA19-9 or its coding gene; and (2) (i) (a) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, SLC27A2, and PTGES2 or their coding genes; and (b) (i) ARG1 or its coding gene, or (ii) one or more selected from the group consisting of TNF, CXCL8, CXCR4, CCR5, and PTGES, or said one or more coding genes. Claim 13 A composition for improving the efficacy of diagnosing pancreatic cancer using CA19-9, wherein the biomarker comprises (1) CA19-9 or its coding gene, and (2) (i) a combination of IFNA1, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCR2, PTGS2, ARG1, SLC27A2, and PTGES2 or their coding genes, or (ii) a combination of IFNA1, TNF, IFNL1, CLEC7A, VEGFA, FOXP3, CCL5, CXCL8, CXCR4, CCR5, CXCR2, PTGS2, SLC27A2, PTGES2, and PTGES, or their coding genes. Claim 14 A composition for improving the efficacy of diagnosing pancreatic cancer using CA19-9, wherein, in any one of claims 11 to 13, the pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN), pancreatic mucinous cystic neoplasm (MCN), or a neuroendocrine tumor of the pancreas. Claim 15 A composition for improving the efficacy of diagnosing pancreatic cancer using CA19-9, wherein, in any one of claims 11 to 13, the pancreatic cancer is one in which the level of CA19-9 protein in the blood is less than 100 U / mL.

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