Methods of detecting and treating colorectal pancreatic ductal adenocarcinoma

Abstract

To provide methods and related kits for detecting early stage pancreatic ductal adenocarcinoma, and methods of treating a patient susceptible, or suspected of being susceptible, to pancreatic ductal adenocarcinoma.SOLUTION: A method of determining susceptibility of a patient to pancreatic ductal adenocarcinoma is provided, the method comprising obtaining a biological sample from the patient, measuring the level of CA19-9 antigen in the biological sample, measuring the level of TIMP1 antigen in the biological sample, and measuring the level of LRG1 antigen in the biological sample, where the amounts of CA19-9 antigen, TIMP1 antigen, and LRG1 antigen classifies the patient as being susceptible to pancreatic ductal adenocarcinoma or not susceptible to pancreatic ductal adenocarcinoma.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. Provisional Patent Application No. 62 / 435,02, filed December 15, 2016. No. 4 and U.S. Provisional Patent Application No. 62 / 435,020. and US Pat. No. 6,229,099, the disclosures of which are incorporated herein by reference in their entireties.

[0002] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention is based on a grant from the National Institutes of Health This work was made with government support under Grant No. R03 CA123546 awarded by the Government. Certain rights are reserved in this invention. [Background technology]

[0003] Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate of only 8%, and mortality closely correlates with morbidity. Resectable PDAC is associated with better survival and Although associated with PDAC, only 15-20% of patients present with localized disease. Currently used in the workup of subjects with or at high risk for this disease are , imaging modalities, particularly endoscopic ultrasound and magnetic resonance cholangiopancreatography. However, known risk factors have only a small effect on PDAC incidence.

[0004] Cancer antigen 19-9 (CA19-9) is currently used clinically as a biomarker for PDAC. CA19-9 is a diagnostic biomarker for both preclinical and early stage PDAC. (Riker et al., Surgical Onco (logy 6:157-69, 1998). However, CA19-9 alone does not Its performance as a biomarker of disease is limited, with fewer than 75% of pancreatic cancer patients having CA19- CA19-9 levels are elevated in patients with a variety of benign disorders. Furthermore, CA19-9 is a fucosyltransferase deficiency disorder associated with Lewis blood group anti- It remains undetectable in 5-10% of patients who are unable to synthesize the antigen, and therefore may be mistakenly diagnosed as having PDAC. The proportion of individuals incorrectly identified as having PDAC and those incorrectly identified as not having PDAC were This makes the reliance on CA19-9 alone as a diagnostic tool unacceptably high. Summary of the Invention [Problem to be solved by the invention]

[0005] Due to delayed diagnosis, increasing incidence and limited treatment options, PDAC accounts for 10% of cancer-related deaths. The disease is generally diagnosed at an advanced stage in many patients, Considering the clear inappropriateness of using CA19-9 as a stand-alone biomarker, Given this, there is a need to develop tests for the detection of pancreatic cancer at an early stage. [Means for solving the problem]

[0006] The present disclosure provides methods and kits for the early detection of pancreatic cancer. The kit uses multiplexed assays of biomarkers in biological samples obtained from subjects. At least three biomarkers: carbohydrate antigen 19-9 (CA19-9) , TIMP metallopeptidase inhibitor 1 (TIMP1) and leucine-rich Combined analysis of LRG1 and LRG2-glycoprotein 1 (LRG1) in a cohort with known status When screened for, this results in a highly accurate diagnosis of PDAC.

[0007] In some embodiments, the levels of the biomarkers CA19-9, TIMP1, and LRG1 The analysis may be combined with the analysis of additional biomarkers. This additional biomarker may be a protein biomarker. In morphology, these additional protein biomarkers include ALCAM, CHI3L1, CO L18A1, IGFBP2, LCN2, LYZ, PARK7, REG3A, SLPI, T HBS1, TNFRSF1A, WFDC2, and any combination thereof In some embodiments, the additional biomarker may be a non-protein biomarker. In some embodiments, the non-protein biomarker can be a In some embodiments, the DNA may be circulating tumor DNA (ctDNA). The method described here involves the detection of (N1 / N8)-acetylspermidine (AcSp) in this biological sample. erm) levels in the biological samples, and diacetylspermine (DAS) levels in the biological samples. ) levels in the biological sample, Measuring the levels of (18:0) lysophosphatidylcholine in this biological sample (LPC)(20:3) levels and indomethacin levels in the biological samples. measuring the level of (N1 / N8)-acetylspermidyl derivatives, AcSperm, diacetylspermine (DAS), lysophosphatidylcholine ( LPC) (18:0), lysophosphatidylcholine (LPC) (20:3) and Indian The amount of hydroxyl derivatives determined whether the patient was susceptible to or not susceptible to pancreatic ductal adenocarcinoma. Therefore, it is classified as not susceptible.

[0008] Levels of CA19-9, TIMP1, and LRG1 found in biological samples from subjects Based on this data, a regression model was developed that could predict the PDAC status of this subject.

[0009] In some embodiments, the biomarkers are measured in a blood sample taken from the patient. In some embodiments, the presence or absence of a biomarker in a biological sample is determined. In some embodiments, the level of a biomarker in a biological sample may be quantified. do.

[0010] In some embodiments, a surface is provided for analyzing a biological sample. In this embodiment, the biomarker of interest is non-specifically adsorbed onto this surface. In some embodiments, the surface is configured with receptors specific for biomarkers of interest. It is included.

[0011] In some embodiments, the surface is associated with a particle (e.g., a bead). In some embodiments, the surface is configured in a multi-well plate to facilitate simultaneous measurements. Included.

[0012] In some embodiments, multiple surfaces are provided for parallel assessment of biomarkers. In some embodiments, the multiple surfaces are provided on a single device, e.g., 96 In some embodiments, the plurality of surfaces is provided in a well plate. In some embodiments, a single biological sample can be used to simultaneously measure multiple proteins. In some embodiments, biocompatible materials may be applied to multiple surfaces simultaneously. The sample is divided.

[0013] In some embodiments, the biomarkers bind to specific receptor molecules and In some embodiments, the presence or absence of a biomarker-receptor complex can be determined. The amount of the receptor-receptor complex can be quantified. The molecule is linked to an enzyme to facilitate detection and quantification.

[0014] In some embodiments, the biomarkers are specific relay molecules. The biomarker-relay molecule complex then binds to the receptor molecule. In some embodiments, the biomarker-relay-receptor complex In some embodiments, the presence or absence of a biomarker-relay-receptor complex can be determined. The amount of incorporation can be quantified. In some embodiments, the receptor molecule is linked to an enzyme. In some embodiments, the enzyme is a Western Horseradish peroxidase or alkaline phosphatase.

[0015] In some embodiments, the biological sample is analyzed sequentially for individual biomarkers. In some embodiments, the biological sample is divided into separate portions to form multiple biopsies. In some embodiments, the biological sample is A number of biomarkers are analyzed in a single process.

[0016] In some embodiments, the presence or absence of a biomarker can be determined by visual inspection. In some embodiments, the amount of a biomarker may be determined by the use of spectroscopic techniques. In some embodiments, the spectroscopic technique is mass spectrometry. In some embodiments, the spectroscopic technique is UV / Vis spectroscopy. The spectroscopic technique is an excitation / emission technique such as fluorescence spectroscopy.

[0017] In some embodiments, kits for the analysis of biological samples are provided. In this embodiment, the kit contains the chemicals and reagents necessary to perform the assay. In some embodiments, the kit may be used to minimize unavoidable operator intervention. The biological sample may be manipulated to contain the biological sample.

[0018] In another aspect, the disclosure provides a method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, the method comprising: obtaining a biological sample from a patient; and Measuring the level of permidine (AcSperm) and diacetyltransferase (DST) in this biological sample The level of lysophosphatidylserine (DAS) in the biological sample was measured. Measuring the levels of lysozyme (LPC) (18:0) in this biological sample Measuring the levels of phosphatidylcholine (LPC) (20:3) and measuring the level of an indole derivative in a sample, AcSpermidine (AcSperm), diacetylspermine (DAS), lysophosphatidylcholine Lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine (LPC) (20:3) and the amount of indole derivatives may determine whether the patient is susceptible to pancreatic ductal adenocarcinoma or whether the patient is The present invention provides a method for classifying a patient as not susceptible to ductal adenocarcinoma.

[0019] In another aspect, the present disclosure provides plasma-derived biomarker panels and protein marker panels. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising administering to said patient a plasma-derived biomarker. The marker panel consisted of (N1 / N8)-acetylspermidine (AcSperm), diacetyltransferase (DTT), and acetyltransferase (ATP). Cetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0), lyso This protein contains phosphatidylcholine (LPC) (20:3) and an indole derivative. The protein biomarker panel included CA19-9, LRG1, and TIMP1. The method includes obtaining a biological sample from the patient, detecting plasma-derived biomarkers in the biological sample, and determining whether the biomarkers are present in the biological sample. plasma-derived biomarkers, including measuring the levels of markers and protein biomarkers; The abundance of markers and protein biomarkers may have identified this patient as susceptible to pancreatic ductal adenocarcinoma. The present invention provides a method for classifying a patient as susceptible to pancreatic ductal adenocarcinoma.

[0020] In another aspect, the present disclosure provides a method for detecting one or more protein biomarkers and one or more The present study aims to assess patient susceptibility to pancreatic ductal adenocarcinoma, including determining the levels of multiple metabolite markers. A method for determining the sex of a patient, comprising obtaining a biological sample from the patient, subjecting the sample to a C contacting the sample with a first reporter molecule that binds to the A19-9 antigen; contacting the sample with a second reporter molecule that binds to the IMP1 antigen; contacting the G1 antigen with a third reporter molecule that binds to the G1 antigen, and determining a level of a biomarker for one or more of the biomarkers; (N1 / N8)-acetylspermidine (AcSperm), diacetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine and indole derivatives, a reporter molecule of the first reporter molecule, a second reporter molecule, a third reporter molecule, and The levels of multiple biomarkers may identify this patient as either susceptible to pancreatic ductal adenocarcinoma or as a pancreatic ductal adenocarcinoma. The present invention provides a method for classifying a patient as not susceptible to adenocarcinoma.

[0021] In another aspect, the disclosure provides a method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, the method comprising: obtaining a biological sample from the patient, and detecting CA19-9 antigen, TIMP, and the like in the biological sample; 1 antigen and LRG1 antigen levels in the biological sample, 8)-Acetylspermidine (AcSperm), diacetylspermine (DAS), Lysophosphatidylcholine (LPC) (18:0), Lysophosphatidylcholine (LPC) (20:3) and one or more metabolites selected from the group consisting of indole derivatives measuring the levels of biological markers, such as CA19-9 antigen, TIMP, 1 antigen, LRG1 antigen, (N1 / N8)-acetylspermidine (AcSperm), di Acetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0), Statistics of levels of lysophosphatidylcholine (LPC) (20:3) and indole derivatives This patient's status was determined to be either susceptible to pancreatic ductal adenocarcinoma, as determined by quantitative analysis, or or assigning the subject to be not susceptible to pancreatic ductal adenocarcinoma.

[0022] In another aspect, the disclosure provides a method for treating a patient suspected of being susceptible to pancreatic ductal adenocarcinoma. and treating the patient with pancreatic ductal adenocarcinoma by the method according to any one of claims 38 to 41. and administering a therapeutically effective amount of treatment to the adenocarcinoma. In one embodiment, the treatment comprises surgery, chemotherapy, radiation therapy. , targeted therapy, or a combination thereof.

[0023] In one embodiment, the methods described herein involve the use of CA19-9, TIMP1 and LR at least one receptor molecule that selectively binds to an antigen selected from the group consisting of G1 Includes.

[0024] In one embodiment, CA19-9, TIMP1, LRG, (N1 / N8)-acetylspar Lumidine (AcSperm), diacetylspermine (DAS), lysophosphatidylcholine Lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine (LPC) (20:3) or Detecting the amount of indole derivatives involves the use of solid particles. The child is a bead.

[0025] In one embodiment, at least one of the reporter molecules is linked to an enzyme.

[0026] In one embodiment, at least one of the protein markers or the metabolite markers is In another embodiment, the detectable signal is generated by spectroscopy. In another embodiment, the spectroscopic method is mass spectrometry. do.

[0027] In one embodiment, the methods described herein are directed to a patient having or with pancreatic ductal adenocarcinoma. This includes including patient medical history information in the assignment of not having a condition.

[0028] In one embodiment, the methods described herein involve treating patients assigned to have pancreatic ductal adenocarcinoma. In another embodiment, the method further comprises administering at least one alternative diagnostic test to the subject. Another alternative diagnostic test involves assaying or sequencing at least one ctDNA. .

[0029] In another aspect, the present disclosure provides a kit for the methods described herein, comprising: First solute for detection of 9-9 antigen, second solute for detection of LRG1 antigen, TIM A third solute, (N1 / N8)-acetylspermidine (AcSp) for the detection of P1 antigen a fourth solute for the detection of erm, and a fourth solute for the detection of diacetylspermine (DAS). 5 solutes, and a sixth solute for the detection of lysophosphatidylcholine (LPC) (18:0) , a seventh solute and ion for the detection of lysophosphatidylcholine (LPC) (20:3) A kit is provided that includes a reagent solution containing an eighth solute for the detection of an androgen derivative.

[0030] In one embodiment, such a kit comprises a first solute for detecting CA19-9 antigen. a first reagent solution containing a second solute for detecting the LRG1 antigen; a third reagent solution containing a third solute for detecting the IMP1 antigen, (N1 / N8)-acetylacetone; a fourth reagent solution containing a fourth solute for the detection of AcSpermidine (AcSperm), A fifth reagent solution containing a fifth solute for the detection of acetylspermine (DAS), lysophosphatase A sixth reagent containing a sixth solute for the detection of liposomal phospholipid (LPC) (18:0) solution, containing a seventh solute for the detection of lysophosphatidylcholine (LPC) (20:3) a seventh reagent solution containing an eighth solute for the detection of indole derivatives; It may contain liquid.

[0031] In one embodiment, the kits described herein include a method for contacting the reagent solution with a biological sample. In another embodiment, such a kit may include at least It may comprise at least one surface having means for binding one antigen. In some cases, the at least one antigen is selected from the group consisting of CA19-9, LRG1, and TIMP1. In another embodiment, the at least one surface is selected from the group consisting of: The method includes means for coupling the

[0032] In another aspect, the present disclosure provides a method as described herein, comprising: To measure the levels of (N1 / N8)-acetylspermidine (AcSperm) in the measuring the level of diacetylspermine (DAS) in the biological sample; Measuring lysophosphatidylcholine (LPC) (18:0) levels in biological samples The level of lysophosphatidylcholine (LPC) (20:3) in this biological sample and measuring the level of an indole derivative in the biological sample. Contains (N1 / N8)-acetylspermidine (AcSperm), diacetylspermidine DAS, lysophosphatidylcholine (LPC) (18:0), lysophosphatidyl The amount of lycolin (LPC) (20:3) and indole derivatives in this patient was compared with that of pancreatic ductal adenocarcinoma. and (c) classifying a patient as susceptible to pancreatic ductal adenocarcinoma or as not susceptible to pancreatic ductal adenocarcinoma. do.

[0033] In another aspect, the disclosure provides a method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, the method comprising: obtaining a biological sample from a patient; and Measuring the level of permidine (AcSperm) and diacetyltransferase (DST) in this biological sample The level of lysophosphatidylserine (DAS) in the biological sample was measured. Measuring the levels of lysozyme (LPC) (18:0) in this biological sample Measuring the levels of phosphatidylcholine (LPC) (20:3) and measuring the level of an indole derivative in a sample, AcSpermidine (AcSperm), diacetylspermine (DAS), lysophosphatidylcholine Lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine (LPC) (20:3) and the amount of indole derivatives may determine whether the patient is susceptible to pancreatic ductal adenocarcinoma or whether the patient is The present invention provides a method for classifying a patient as not susceptible to ductal adenocarcinoma.

[0034] In another aspect, the present disclosure provides plasma-derived biomarker panels and protein marker panels. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising administering to said patient a plasma-derived biomarker. The marker panel consisted of (N1 / N8)-acetylspermidine (AcSperm), diacetyltransferase (DTT), and acetyltransferase (ATP). Cetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0), lyso This protein contains phosphatidylcholine (LPC) (20:3) and an indole derivative. The protein biomarker panel included CA19-9, LRG1, and TIMP1. The method includes obtaining a biological sample from the patient, detecting plasma-derived biomarkers in the biological sample, and determining whether the biomarkers are present in the biological sample. plasma-derived biomarkers, including measuring the levels of markers and protein biomarkers; The abundance of markers and protein biomarkers may have identified this patient as susceptible to pancreatic ductal adenocarcinoma. The present invention provides a method for classifying a patient as susceptible to pancreatic ductal adenocarcinoma.

[0035] In another aspect, the present disclosure provides a method for detecting one or more protein biomarkers and one or more The present study aims to assess patient susceptibility to pancreatic ductal adenocarcinoma, including determining the levels of multiple metabolite markers. A method for determining the sex of a patient, comprising obtaining a biological sample from the patient, subjecting the sample to a C contacting the sample with a first reporter molecule that binds to the A19-9 antigen; contacting the sample with a second reporter molecule that binds to the IMP1 antigen; contacting the G1 antigen with a third reporter molecule that binds to the G1 antigen, and determining the level of a biomarker of the one or more biomarkers (N1 / N8)-acetylspermidine (AcSperm), diacetylspermidine DAS, lysophosphatidylcholine (LPC) (18:0), lysophosphatidyl choline (LPC) (20:3) and indole derivatives, a first reporter molecule, a second reporter molecule, a third reporter molecule, and the amount of one or more biomarkers may identify this patient as susceptible to pancreatic ductal adenocarcinoma. The present invention provides methods for classifying a patient as susceptible to or not susceptible to pancreatic ductal adenocarcinoma.

[0036] In another aspect, the disclosure provides a method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, the method comprising: obtaining a biological sample from the patient, and detecting CA19-9 antigen, TIMP, and the like in the biological sample; 1 antigen and LRG1 antigen levels in the biological sample, and 1 / N8)-acetylspermidine (AcSperm), diacetylspermine (DAS) ), lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine (L PC) (20:3) and indole derivatives Measuring the levels of metabolite markers, CA19-9 antigen, T IMP1 antigen, LRG1 antigen, (N1 / N8)-acetylspermidine (AcSperm ), diacetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0 ), lysophosphatidylcholine (LPC) (20:3) and indole derivative levels This patient's status was determined to be susceptible to pancreatic ductal adenocarcinoma as determined by statistical analysis of the and assigning the patient to either be susceptible to pancreatic ductal adenocarcinoma or not susceptible to pancreatic ductal adenocarcinoma. to provide.

[0037] In another aspect, the disclosure provides a method for treating a patient suspected of being susceptible to pancreatic ductal adenocarcinoma. and treating the patient with pancreatic ductal adenocarcinoma by the method according to any one of claims 36 to 39. and administering a therapeutically effective amount of treatment to the adenocarcinoma. In one embodiment, the treatment comprises surgery, chemotherapy, radiation therapy. In another embodiment, such a method is a method for treating CA1 A small molecule that selectively binds to an antigen selected from the group consisting of 9-9, TIMP1, and LRG1. In another embodiment, the antibody comprises at least one receptor molecule, such as CA19-9, TIMP1, LRG, (N1 / N8)-acetylspermidine (AcSperm), diacetylsperm DAS, lysophosphatidylcholine (LPC) (18:0), lysophosphatidyl Detection of the amount of lysozyme (LPC) (20:3) or indole derivatives using solid particles In another embodiment, the solid particles are beads. In another embodiment, at least one of the control molecules is linked to an enzyme. At least one of the quality markers or the metabolite markers generates a detectable signal. In another embodiment, the detectable signal is detectable by spectrometry. In another embodiment, the spectroscopic method is mass spectrometry. Such a method involves assigning patients to have or not have pancreatic ductal adenocarcinoma. In another embodiment, such methods include the inclusion of medical history information. In another embodiment, the method further comprises administering at least one alternative diagnostic test to the assigned patient. The at least one alternative diagnostic test shall include at least one ctDNA assay or sequence. Includes column determination.

[0038] In another aspect, the present disclosure provides a kit for the method according to any one of claims 36 to 40. a first solute for detecting CA19-9 antigen, a second solute for detecting LRG1 antigen, The second solute, a third solute, (N1 / N8)-acetylsparaffin, for the detection of TIMP1 antigen. A fourth solute, diacetylspermine (DAS), for the detection of lumidine (AcSperm) ) for the detection of a fifth solute, lysophosphatidylcholine (LPC) (18:0) For the detection of the sixth solute, lysophosphatidylcholine (LPC) (20:3) A kit containing a reagent solution containing a seventh solute and an eighth solute for the detection of indole derivatives. In another embodiment, the kit disclosed herein provides a method for detecting CA19-9 antigen. a first reagent solution containing a first solute for detection, a second solute for detection of the LRG1 antigen; a second reagent solution containing a third solute for detecting the TIMP1 antigen; , a fourth solute for the detection of (N1 / N8)-acetylspermidine (AcSperm) a fourth reagent solution containing a fifth solute for the detection of diacetylspermine (DAS); A fifth reagent solution containing lysophosphatidylcholine (LPC) (18:0) A sixth reagent solution containing six solutes, lysophosphatidylcholine (LPC) (20:3), was used as a test solution. a seventh reagent solution containing a seventh solute for the detection of indole derivatives and an eighth reagent solution containing a seventh solute for the detection of indole derivatives; In one embodiment, such a kit includes an eighth reagent solution containing a solute. In another embodiment, such a device includes a device for contacting a liquid with a biological sample. The kit comprises at least one surface having means for binding at least one antigen. In another embodiment, the at least one antigen is CA19-9, LRG1, and T In another embodiment, the at least one surface is selected from the group consisting of: It comprises a means for binding to ctDNA.

[0039] In another aspect, the disclosure provides a method for treating or preventing the progression of pancreatic ductal adenocarcinoma (PDAC) in a patient. The method, wherein the levels of CA19-9 antigen, TIMP1 antigen and LRG1 antigen are measured by the method. The patient is classified as having or susceptible to PDAC, and The aim of this study was to provide a method for administering chemotherapy to patients with PDAC, and to provide a method for administering therapeutic radiation to patients with PDAC. administering radiation and performing partial or complete surgery of cancerous tissue in patients with PDAC In one embodiment, the method comprises one or more surgical procedures for selective removal of the tumor. The levels of A19-9 antigen, TIMP1 antigen, and LRG1 antigen are elevated. In an embodiment, the levels of CA19-9 antigen, TIMP1 antigen and LRG1 antigen are determined to be indicative of PDA. CA19-9 antigen, TIMP1 antigen, and LRG in reference patients or groups without C In another embodiment, the level of the reference patient or reference group is elevated compared to the level of the reference antigen. In another embodiment, the AUC(95% CI) is at least 0.850. In another embodiment, the AUC(95% CI) is at least 0.900. In embodiments, the classification of a patient as having PDAC has a specificity of 95% and 99%, respectively. In another embodiment, the CA19-9 antigen has a sensitivity of 0.849 and 0.658. , TIMP1 antigen and LRG1 antigen levels were measured in reference patients with chronic pancreatitis or in reference The levels of CA19-9 antigen, TIMP1 antigen, and LRG1 antigen were elevated compared with those of the control group. In another embodiment, the levels of CA19-9 antigen, TIMP1 antigen, and LRG1 antigen are Bell will compare the CA19-9 antigen, TIMP1 in reference patients or reference groups with benign pancreatic disease. In another embodiment, the AUC( 95%CI) is at least 0.850. ) is at least 0.900. In another embodiment, the patient is diagnosed as having PDAC. The classes have sensitivities of 0.849 and 0.658 at specificities of 95% and 99%, respectively. In another embodiment, the PDAC is borderline resectable. In another embodiment, the disease is diagnosed at or before the stage of development. In most cases, PDAC is diagnosed at a resectable stage.

[0040] In another aspect, the disclosure provides a method for treating or preventing the progression of pancreatic ductal adenocarcinoma (PDAC) in a patient. A method comprising: Spermidine (AcSperm), diacetylspermine (DAS), lysophosphatidylinositol Lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine (LPC) (20:3) and The levels of ribonucleotides and indole derivatives were used to characterize the patient as having or being susceptible to PDAC. This method classifies patients with PDAC as sensitive to chemotherapy, and administering therapeutic radiation to patients with PDAC; and administering therapeutic radiation to patients with PDAC. The procedure involves one or more surgical procedures for partial or complete surgical removal of cancerous tissue in a patient. In another embodiment, a method is provided in which a CA19-9 antigen, a TIMP1 antigen, and an LRG In another embodiment, the level of CA19-9 antigen, TIMP1 antigen, or the like is elevated. The levels of CA1 and LRG1 antigens were compared with those of reference patients or groups without PDAC. The levels of 9-9 antigen, TIMP1 antigen, and LRG1 antigen are elevated compared to other antigens. In an embodiment, the reference patient or group is healthy. The levels of -9 antigen, TIMP1 antigen and LRG1 antigen were compared with those of reference patients with chronic pancreatitis. or compared with the levels of CA19-9 antigen, TIMP1 antigen, and LRG1 antigen in the reference group. In another embodiment, the CA19-9 antigen, the TIMP1 antigen, and the LRG1 antigen are elevated. The antigen levels were measured using the CA19-9 antigen, T antigen, and IL-16 antigen in reference patients or reference groups with benign pancreatic disease. In another embodiment, the levels of the IMP1 antigen and the LRG1 antigen are elevated. In another embodiment, the patient is at high risk for PDAC. Patients over 50 years of age with a new onset of the disease, those with chronic pancreatitis, or those incidentally diagnosed with a mucin-secreting cyst of the pancreas have been diagnosed or are asymptomatic kindreds of one of these high-risk groups.

[0041] In another aspect, the disclosure provides a method for treating a patient suspected of being susceptible to pancreatic ductal adenocarcinoma. and the patient is screened for susceptibility to pancreatic ductal adenocarcinoma by the methods described herein. and administering a therapeutically effective amount of treatment to the adenocarcinoma. In some cases, the treatment may include surgery, chemotherapy, radiation therapy, targeted therapy, or a combination thereof. is. [Brief explanation of the drawings]

[0042] [Figure 1] 1 shows a flowchart for discovering validated biomarker models. [Figure 2A-B] Biomarker candidates with significantly higher levels in PDAC compared to healthy controls in the triage set are shown. Performance of biomarker candidates in (Figure 2A) PDAC (n=75) vs. healthy controls (n=27) and (Figure 2B) PDAC vs. chronic pancreatitis patients (n=19) in the triage set. Bars indicate AUC (95% CI). * indicates reverse ordering was used. AUC, area under the curve. [Figure 3A-B]Figure 3 shows the performance of the biomarker panel based on TIMP1 + LRG1 + CA19-9 in the combined validation set. ROC analysis of the developed biomarker panel for (Figure 3A) PDAC vs. healthy controls and (Figure 3B) PDAC vs. benign pancreatic disease (combined "OR" rule). The upper line represents the model, and the lower line represents CA19-9. AUC, area under the curve. [Figure 4] Correlation analysis between biomarker panel (TIMP1, LRG1, and CA19-9)-based scores and tumor size values in validation set #2. The underlying linear regression model had an intercept and slope of 3.7329 and -0.2646, respectively (95% CI for slope = -0.745 to 0.216; Wald-based two-sided p-value = 0.27). Tumor size refers to the larger of the two measurements assessed by CT / MRI / EUS. [Figure 5] Figure 1 shows the performance of a biomarker model based on TIMP1 + LRG1 + CA19-9 in the test set. ROC analysis of the combined model with fixed coefficients developed in the combined validation set for PDAC versus healthy controls. The upper line represents the model, and the lower line represents CA19-9. AUC, area under the curve. [Figure 6] The study design and filtering strategy are outlined. [Figure 7] Individual AUCs for the detection of lysophosphatidylcholine, sphingomyelin, and ceramide in the discovery cohort are shown. Abbreviations: LPC: lysophosphatidylcholine; SM: sphingomyelin. [Figure 8] The MSMS spectrum for the indole derivative is shown, with the corresponding fragments occurring at about 118, about 148 and about 188 m / z. [Figure 9A-B]Figure 9 shows the AUC curves for individual metabolites and the 5-marker metabolite panel in the training set. Performance based on the combined discovery and "validation" cohort. (Figure 9A) Receiver operating characteristic (ROC) curves for individual metabolites and the 5-marker metabolite panel for the discrimination of PDAC (n=29) from healthy subjects (n=10). (Figure 9B) ROC curves for individual metabolites and the 5-marker metabolite panel comparing PDAC (n=29) to subjects diagnosed with benign pancreatic disease: chronic pancreatitis (n=10) and low-grade cysts (n=50). [Figure 10A-B] Validation of individual metabolites and a five-marker metabolite panel in test sets is shown. (FIG. 10A) Receiver operating characteristic (ROC) curves for individual metabolites and a five-marker metabolite panel for distinguishing resectable PDAC (n=39) from healthy subjects (n=82) (Test Set #1). (FIG. 10B) ROC curves for individual metabolites and a five-marker metabolite panel comparing resectable PDAC (n=20) to subjects diagnosed with benign pancreatic disease (low-grade cysts (n=102)) (Test Set #2). [Figure 11A-B] A hyperpanel consisting of a metabolite panel and a protein panel improves classification compared to the protein panel alone (Figures 11A and 11B). ROC curves for the hyperpanel and the protein panel alone in the training set (29 PDAC vs. 10 healthy subjects) and an independent validation cohort (Test Set #1; 39 PDAC vs. 82 healthy subjects). [Figure 12A-C]Pancreatic ductal adenocarcinoma demonstrates catabolism of extracellular lysophospholipids. (Figure 12A) Percent change in serum-containing medium composition of lysophosphatidylcholine (18:0), lysophosphatidylcholine (20:3), and glycerophosphocholine in PANC1 and SU8686 PDAC cell lines after 24, 48, and 72 hours of culture. (Figure 12B) Overview of enzymes involved in phosphatidylcholine and lysophosphatidylcholine catabolism. (Figure 12C) mRNA expression of PLA2G10, LYPLA1, and ENPP2 in PDAC and adjacent control tissues + / - SEM. Statistical significance was determined by paired t-test (p: **<0.01, ****<0.001). mRNA expression data obtained from Oncomine and based on the Badea dataset. [Figure 13] Figure 1 shows the composition of lipid species in conditioned medium. Heat map showing the % change in lipid species composition in serum-containing medium from PDAC cell lines PANC-1 and SU8686 after 24, 48, and 72 hours of conditioning compared to medium blank. Abbreviations: PC: phosphatidylcholine; PE: phosphatidylethanolamine; LPC: lysophosphatidylcholine; LPE: lysophosphatidylethanolamine; Plas: plasmalogen. [Figure 14A-C]Pancreatic ductal adenocarcinomas show elevated polyamine catabolism. (Figure 14A) Abundance of N1 / N8-acetylspermidine or diacetylspermine (unit area ± stDev) in cell lysates from five PDAC cell lines (CFPAC-1, MiaPaCa, SU8686, PANC03-27, and SW1990). (Figure 14B) Abundance of N1 / N8-acetylspermidine or diacetylspermine (unit area ± stDev) in serum-free medium collected 1, 2, 4, and 6 hours after conditioning from five PDAC cell lines (CFPAC-1, MiaPaCa, SU8686, PANC03-27, and SW1990). (Figure 14C) Network displaying enzymes involved in the biosynthesis of polyamines and their acetylated derivatives. Node shading (light gray = decrease; dark gray = increase) and size indicate the direction and magnitude of change in mRNA expression of each enzyme between PDAC and adjacent control tissue. Thick node boundaries indicate statistical significance (paired t-test <0.05). Box plots show the distribution of mRNA expression of each enzyme between PDAC and adjacent control tissues. mRNA expression data were obtained from Oncomine and are based on the Badea dataset. DETAILED DESCRIPTION OF THE INVENTION

[0043] Provided is a method for identifying pancreatic cancer in a human subject, comprising: (a) analyzing a blood sample obtained from the subject for at least three biomarkers: CA1 9-9, applying it to assays for the analysis of TIMP1 and LRG1; (b) quantifying the amount of at least three biomarkers present in the blood sample; thing, (c) Applying statistical analysis based on the amount of biomarkers present to identify the corresponding pancreatic cancer-related and determining a biomarker score corresponding to the subject's pancreatic cancer risk, thereby determining whether the subject is positive or negative for pancreatic cancer. To classify into either The method generally comprises:

[0044] The methods herein allow for screening of high-risk subjects, e.g., those with a family history of pancreatic cancer. Presence of or other risk factors (e.g., chronic pancreatitis, obesity, heavy smoking, and possibly The logistics provided herein allow for screening of patients with diabetes. A block regression model can incorporate these factors into the classification method.

[0045] Further methods for clarifying PDAC status for subjects classified as PDAC positive After classification as PDAC positive, computed tomography (CT), endoscopic ultrasonography, These include endoscopic ultrasonography (EUS) or endoscopic retrograde cholangiopancreatography (ERCP), Non-limiting methods may be followed.

[0046] Detection of CA19-9 was performed using the sequence Neu5Acα2,3Galβ1,3(Fucα1,4) GlcNAc-bearing sialyl-Lewis A (a member of the Lewis family of blood group antigens) This can be achieved by contact with the CA19-9 antigen, a carbohydrate structure called the sialic acid moiety. Lewis A sequentially converts monosaccharide precursors into both N-linked and O-linked glycans. It is synthesized by glycosyltransferases that link sialyl-Lewis A The antibodies bind to many different proteins (e.g., mucins, carcinoembryonic antigens, and circulating apolipoproteins). attached to circulating apolipoprotein In the standard CA19-9 clinical assay, monoclonal antibodies are used in sandwich ELISA. The CA19-9 antigen was captured and detected in the A format, and this sandwich ELISA format The assay measures the CA19-9 antigen on a number of different carrier proteins (Par Tyka et al.,Proteomics 12(13):2213-20,20 12).

[0047] Detection of TIMP1 (SEQ ID NO: 1; UniProtKB: P01033) was performed using TIMP1 This may be achieved by contacting the protein with a reporter molecule which specifically binds to the protein. [ka]

[0048] Detection of LRG1 (SEQ ID NO: 2; UniProtKB: P02750) was performed using a ELISA specific for LRG1. This may be achieved by contact with a heterologously linked reporter molecule. [ka]

[0049] A combination of at least three biomarkers: CA19-9, TIMP1, and LRG1 This provides unprecedentedly reliable predictive power for PDAC. The study consisted of plasma samples from 82 eligible PDAC cases and 82 matched healthy controls. When applied to a blinded study set, the methods described herein can be used to assess the efficacy and safety of health control subjects. 0.667 with a sensitivity of 95% specificity in identifying early stage PDAC compared with 0. The AUC (95% CI) was 887 (0.817-0.957). The performance of CA-panel is statistically significant compared to CA19-9 alone, with high accuracy in detecting early-stage pancreatic cancer. showed a significant improvement (p=0.008, test set).

[0050] With respect to the detection of the biomarkers detailed herein, the present disclosure relates to the detection of the biomarkers reported herein. In some embodiments, the biomarkers of the present disclosure are not limited to the specific biomarkers. Other biomolecules can be selected for detection and analysis of proteins, These include biomolecules based on proteins, antibodies, nucleic acids, aptamers and synthetic organic compounds, Other molecules may be used to improve sensitivity, efficiency, assay speed, cost, safety, or manufacturing. Alternatively, it may present advantages in terms of ease of storage. The predictive and diagnostic power of biomarkers depends not only on the protein form of the biomarker, but also on the and may even extend to the analysis of other manifestations of this biomarker (e.g., nucleic acids). Those skilled in the art will recognize that the predictive power and The diagnostic power may also be used in combination with the analysis of other biomarkers associated with PDAC. Those skilled in the art will recognize that in some embodiments, other biomarkers associated with PDAC may be used. In some embodiments, the biomarker may be a protein-based biomarker. Other biomarkers associated with PDAC include non-protein-based biomarkers (e.g., For example, ctDNA).

[0051] TIMP1 and LRG1 were associated with CA19- Increased gene expression and / or secretion of TIMP1 has already been shown in PDAC. It has been observed in tumors and is known to induce tumor cell proliferation. Elevated levels are associated with PDAC, but elevated levels are also seen in other epithelial tumor types. The role of LRG1 has been shown to promote angiogenesis through activation of the TGF-β pathway. In addition to PDAC, increased plasma levels of LRG1 have been found in other cancer types. are.

[0052] The performance of the three-marker panel was compared in early-stage PDAC and matched healthy subjects or benign pancreatic disease. Demonstrated a statistically significant improvement over CA19-9 alone in discrimination from controls This three-marker panel is not suitable for screening of average-risk asymptomatic subjects. In contrast, subjects at high risk (i.e., those with family history, cystic lesions, chronic pancreatitis) or It allows for the assessment of PDAC among subjects presenting with adult-onset type II diabetes.

[0053] Disclosed herein are methods for treating resectable PDAC patients and matched controls. Sequential screening of identified biomarker candidates in multiple independent sets of samples from To perform the validation, we used both human pre-diagnostic and mouse early stage PDAC plasma samples. This is the first proteomics-based study to be performed using

[0054] In some embodiments, CA19-9, TIMP1, and LRG1 in a biological sample In some embodiments, the levels of CA19-9, TIMP1, and LRG are measured. 1 is contacted with a reporter molecule and the level of each reporter molecule is measured. In some embodiments, antibodies that specifically bind to CA19-9, TIMP1, and LRG1, respectively, are used. Three reporter molecules are provided. The use of reporter molecules improves the convenience of the assay. and sensitivity may be increased.

[0055] In some embodiments, CA19-9, TIMP1, and LRG1 are provided in the kit. In some embodiments, the reporter molecule is adsorbed onto a surface. The biomarkers bind to the non-antibody-specific CA19-9, TIMP1, and LRG1. In some embodiments, the surface may be one or more The biomarkers contain receptor functional groups to increase selectivity for adsorption of the biomarkers.

[0056] In some embodiments, CA19-9, TIMP1, and LRG1 are selected from these biomarkers. The antibodies are then adsorbed onto three surfaces selective for one or more of the markers. A reporter molecule or molecules are attached to the biomarker adsorbed on the surface The level of reporter molecules associated with a particular surface can be determined by measuring the specific surface activity present on that surface. This may allow for easy quantification of biomarkers.

[0057] In some embodiments, CA19-9, TIMP1, and LRG1 are provided in the kit. The antibodies are adsorbed onto the surface and specific for one or more of these biomarkers. One or more relay molecules can bind to the biomarkers adsorbed on the surface. Relay molecules can bind to specific receptor molecules. The receptor molecule may confer specificity for the car and allow for detection.

[0058] In some embodiments, CA19-9, specific for TIMP1 and LRG1, respectively Three types of relay molecules that bind to the biomarkers are prepared. These can be specifically designed for binding or selected from a pool of candidates due to their binding properties. These relay molecules can be generated to bind to these biomarkers. It may be an antibody.

[0059] In some embodiments, CA19-9, TIMP1, and LRG1 are provided in the kit. The antibodies were adsorbed onto three distinct surfaces and specific for one or more of these biomarkers. A suitable relay molecule can bind to the biomarker adsorbed on the surface, and a receptor molecule can bind to the biomarker. The analysis of these surfaces can be accomplished stepwise or simultaneously.

[0060] In some embodiments, the reporter molecule is linked to an enzyme, and the reporter In some embodiments, quantification is facilitated by determining the number of molecules that have desirable spectroscopic properties. This can be achieved by catalytic production of substances that

[0061] In some embodiments, the amount of the biomarker is determined using spectroscopy. In some embodiments, the spectroscopy is UV / visible spectroscopy. The amount of the biomarker is determined using mass spectrometry.

[0062] Can the amount of biomarker found in a particular assay be reported directly to the operator? or stored digitally and readily available for mathematical processing. A system may be provided for classifying patients as PDAC positive or PDAC negative. The results may be further reported to the regulator.

[0063] In some embodiments, additional assays known to those of skill in the art will function within the scope of this disclosure. Other exemplary assays include, but are not limited to, assays utilizing: Not included: Mass spectrometry, immunoaffinity LC-MS / MS, surface plasmon resonance, chromatograhy raffin, electrochemical, sonic, immunohistochemical and array techniques.

[0064] Also provided herein are methods of treating subjects typed as PDAC positive. Treatment for PDAC-positive patients includes surgery, chemotherapy, radiation therapy, targeted therapy, or a combination of these. Examples include, but are not limited to, combinations of the above.

[0065] The above has outlined the features and technical advantages of the present disclosure so that the detailed description may be better understood. The specific embodiments disclosed are intended to carry out the same objectives of the present disclosure. It is understood that the invention may be readily used as a basis for the modification or design of other structures or processes for Those skilled in the art should recognize that the present disclosure is not limited to the particular embodiments described. It should be understood that variations of this particular embodiment may be made and are within the scope of the appended claims. Because it still falls within the scope of the claims.

[0066] definition As used herein, the term "pancreatic cancer" refers to a malignancy of the pancreas characterized by abnormal cell growth. refers to a neoplasm in which the proliferation of these cells exceeds the proliferation of the normal tissue surrounding the cells and This is not in line with normal cell proliferation.

[0067] As used herein, the term "PDAC" refers to pancreatic ductal adenocarcinoma, which is It is a pancreatic cancer that can arise in the ducts of the pancreas.

[0068] As used herein, the term "PDAC positive" refers to the determination of a subject as having PDAC. Refers to a similar thing.

[0069] As used herein, the term "PDAC-negative" refers to a subject who does not have PDAC. Refers to classification.

[0070] As used herein, the term "pancreatitis" refers to inflammation of the pancreas. Pancreatitis is generally associated with cancer. is not classified as pancreatic cancer, but may progress to pancreatic cancer.

[0071] As used herein, the term "subject" or "patient" refers to a If so, classification as PDAC-positive or PDAC-negative is desired and further treatment is indicated. It refers to mammals (preferably humans) to whom the treatment can be administered.

[0072] As used herein, a "reference patient" or "reference group" refers to a patient who has PDAC or or test samples from patients suspected of being susceptible to PDAC were compared. In some embodiments, such a comparison is performed using a group of test subjects. A reference patient or group can be used to determine whether a patient has PDAC. It may serve as a control for testing or diagnostic purposes. In this case, the reference patient or group may be a sample obtained from a single patient, or may be a group of samples. The sample may represent a group of samples (e.g., a pooled group of samples).

[0073] As used herein, "healthy" refers to an individual with a healthy pancreas, i.e., normal A healthy patient or subject is an individual with intact pancreatic function. In some embodiments, a healthy patient or subject has: A sample of a patient or group of patients for the determination of PDAC or suspected PDAC is obtained. They can be used as reference patients for comparison with samples.

[0074] The term "treatment" or "treating" as used herein refers to a subject or patient. Prevention of the onset or recurrence of infirmity, or disease, condition, or event, if the patient is suffering from (prophylaxis) (prevention), or the onset or or the administration of a drug or other treatment to this subject for the purpose of curing or reducing the severity or likelihood of recurrence. refers to the performance of a medical procedure. In the context of this disclosure, the term refers to the administration of a pharmacological substance or formulation. administration or non-pharmacological methods (e.g., but not limited to, radiation therapy and surgery) As used herein, pharmacological agents may include, but are not limited to: These include, but are not limited to, chemotherapeutic agents established in the art, such as gemcitabine (G EMZAR), 5-fluorouracil (5-FU), irinotecan (CAMPTOSAR) ), oxaliplatin (ELOXATIN), albumin-bound paclitaxel (ABRA XANE), capecitabine (XELODA), cisplatin, paclitaxel (TAXO L), docetaxel (TAXOTERE) and irinotecan liposome (ONIVYD E) Pharmacological agents include those used in immunotherapy (e.g., checkpoint inhibitors). Treatment may include various pharmacological agents or various treatment methods (e.g., but not limited to, may include surgery and chemotherapy).

[0075] As used herein, the term "ELISA" refers to enzyme-linked immunosorbent assay The assay generally involves the addition of a fluorescently tagged sample of a protein to a target protein. The detection of this protein involves contacting it with an antibody having specific affinity for the protein. This can be accomplished by a variety of means, including, but not limited to, laser fluorimetry.

[0076] As used herein, the term "regression" refers to the regression of an observable trait (or traits) of a sample. Based on the set of possible traits, a predicted value is assigned for the basic characteristics of the sample. In some embodiments, the property is not directly observable but is a statistical method that can be used to estimate the property. For example, the regression methods used herein may be used to identify specific biomarkers for a particular subject. - qualitative or quantitative results of a test or set of biomarker tests, It may be associated with the possibility of being DAC positive.

[0077] As used herein, the term "logistic regression" refers to the distribution of predictions from a model. refers to a regression method in which the fit can have one of several allowable discrete values. For example, as used herein, The logistic regression model used determines whether a particular subject is PDAC-positive or PDAC-negative. Either prediction of sex may be assigned.

[0078] As used herein, the term "biomarker score" refers to a specific biomarker score for a particular subject. and a statistical method for calculating a biomarker level for the subject. Refers to a numerical score that indicates

[0079] As used herein, the term "cutoff point" refers to a point at which a subject's biomarkers are measured. used to assign a PDAC-positive or PDAC-negative classification to the subject based on the core It refers to a mathematical value associated with a specific statistical method that can be used.

[0080] As used herein, the term "classification" refers to the classification of biomarkers obtained for a subject. Assignment of the subject as either PDAC-positive or PDAC-negative based on the results of the core This refers to the assignment.

[0081] As used herein, the term "PDAC positive" refers to a patient who is PDAC positive based on the outcome results of the disclosed methods. This refers to an index based on which a subject is predicted to be sensitive to PDAC.

[0082] As used herein, the term "PDAC-negative" refers to a patient who is PDAC-negative based on the outcome results of the disclosed methods. It refers to an indicator that predicts that a subject will not be susceptible to PDAC based on the above.

[0083] As used herein, the term "Wilcoxon rank sum test" (Mann-Whitney tney U test, Mann-Whitney-Wilcoxon test or Wilco The variance test (also known as the Mann-Whitney test) is used to compare two populations. For example, this test is used herein to measure the degree of certainty of an observable To correlate identifiable traits (particularly biomarker levels) with the presence or absence of PDAC in subjects of specific populations. It is possible.

[0084] As used herein, the term "true positive rate" refers to the percentage of cases classified as positive by a particular method. Refers to the probability that a given subject is a true positive.

[0085] As used herein, the term "false positive rate" refers to the percentage of cases classified as positive by a particular method. Refers to the probability that a given subject is truly negative.

[0086] As used herein, the term "ROC" refers to the ratio of a specific 1 is a graphical plot used herein to evaluate the performance of a diagnostic method; ROC plots show true and false positives at various cutoff points. It can be constructed from the ratio of

[0087] As used herein, the term "AUC" refers to the area under the curve of a ROC plot. The AUC can be used to estimate the predictive power of a particular diagnostic test. Generally, a larger AUC indicates A decrease in the frequency of prediction errors corresponds to an increase in predictive power. Possible values of AUC range from 0.5 to 1 0, the latter value being characteristic of an error-free prediction method.

[0088] As used herein, the term "p-value" or "p" refers to the number of subjects with positive PDAC and Distribution of biomarker scores for subjects with non-positive PDAC was determined by Wilcoxon rank sum analysis p-values near zero generally indicate that a particular statistical method is We show that it may have high predictive power in elephant classification.

[0089] As used herein, the term "CI" refers to a confidence interval (i.e., the interval at which a particular value falls within a particular confidence level). As used herein, the term "95% CI" refers to the interval at which a given value can be predicted to lie. " refers to the interval within which a particular value can be predicted at a 95% confidence level.

[0090] As used herein, the term "sensitivity" refers to the ability of a disease to be detected in relation to various biochemical assays. This refers to the ability of an assay to correctly identify those with a disease (i.e., true positive rate). As used herein, the term "specificity" refers to the ability of a patient to identify a disease in relation to various biochemical assays. Sensitivity and specificity refer to the ability of an assay to correctly identify those who do not have the disease (i.e., the true negative rate). The degree of accuracy is a statistical measure of the power (i.e., classification function) of a binary classification test. Sensitivity is the avoidance of false negatives. Quantifying specificity does the same with monitoring false positives.

[0091] As used herein, the term "ALCAM" refers to activated leukocyte cell adhesion molecule .

[0092] As used herein, the term "CHI3L1" refers to chitinase-3-like-1.

[0093] As used herein, the term "COL18A1" refers to type XVIII collagen alfa. Point to Fa1.

[0094] As used herein, the term "IGBFP2" refers to insulin-like growth factor binding protein (IGFBP). Refers to protein 2.

[0095] As used herein, the term "LCN2" refers to lipocalin 2.

[0096] As used herein, the term "LRG1" refers to a leucine-rich alpha-2-glycoprotein. It refers to protein 1.

[0097] As used herein, the term "LYZ" refers to lysozyme 2.

[0098] As used herein, the term "PARK7" refers to a protein deglycase (pro This refers to tein deglycase (DJ-1).

[0099] As used herein, the term "REG3A" refers to regenerative family member 3 alpha (regenerating family member 3 alpha).

[0100] As used herein, the term "SLPI" refers to antileukoproteinase (an Secretory leukocyte proteinase, also known in the art as leukocyte leukocyte proteinase (LEP), is a protein that binds to the leukocytes. This refers to protease inhibitors.

[0101] As used herein, the term "pro-CTSS" refers to procathepsin S.

[0102] As used herein, the term "total-CTSS" refers to total cathepsin S.

[0103] As used herein, the term "THBS1" refers to thrombospondin 1.

[0104] As used herein, the term "TIMP1" refers to a metalloproteinase inhibitor. TIMP metallopeptidase inhibitor 1, also known in the art as TIMP metallopeptidase inhibitor 1 .

[0105] As used herein, the term "TNFRSF1A" refers to the tumor necrosis factor receptor serotype. Refers to Superfamily member 1A.

[0106] As used herein, the term "WFDC2" refers to a WAP 4-disulfide core adduct. Refers to Main 2.

[0107] As used herein, the term "CA19-9" refers to carbohydrate antigen 19-9, Cancer antigen 19-9 and sialylated Lewis a Also known in the art as an antigen.

[0108] As used herein, the term "ctDNA" refers to cell-free DNA or circulating tumor DNA. ctDNA refers to DNA that has been found to circulate freely in the blood of cancer patients. Without being limited by theory, ctDNA is the DNA of dying tumor cells. It is thought to arise from the cytoplasm and is widespread, albeit at various levels and mutant allele fractions. In general, ctDNA is formed in the tumor cells of origin and is associated with the health of the host. The ctDNA somatic mutations are unique and not found in normal cells. , can act as a cancer-specific biomarker.

[0109] As used herein, a "metabolite" refers to an intermediate and / or product of cellular metabolism. Metabolites are small molecules that can perform a variety of functions in cells, such as providing structural support to enzymes. They can exert structural, signaling, stimulatory and / or inhibitory effects. In this embodiment, the metabolite may be a non-protein plasma-derived metabolite marker, Markers include, for example, acetylspermidine, diacetylspermine, lysophosphatidylcholine lysophosphatidylcholine (18:0), lysophosphatidylcholine (20:3), and indole derivatives These include, but are not limited to, the body.

[0110] As used herein, "indole derivative" refers to a compound derived from indole. Indole is an aromatic heterocyclic organic compound with the formula C8H7N. It has a cyclic structure, consisting of a six-membered benzene ring fused to a five-membered nitrogen-containing pyrrole ring. The indole derivatives described herein can be any derivative of indole. Examples include, but are not limited to: tryptophan, indole-3 -ethanol, 10,11-methylenedioxy-20(S)-CPT, 9-methyl-20 (S)-CPT, 9-amino-10,11-methylenedioxy-20(S)-CPT, 9 -chloro-10,11-methylenedioxy-20(S)-CPT, 9-chloro-20(S )-CPT, 10-hydroxy-20(S)-CPT, 9-amino-20(S)-CPT , 10-amino-20(S)-CPT, 10-chloro-20(S)-CPT, 10-nitro rho-20(S)-CPT, 20(S)-CPT, 9-hydroxy-20(S)-CPT, (SR)-Indoline-2-carboxylic acid, IAA, IAA-L-Ile, IAA-LL eu, IBA, ICA-OEt, ICA, indole-3-acrylic acid, indole-3 -carboxylic acid methyl ester, indole-3-carboxylic acid, indole-4-carboxylic acid Acid methyl ester, Boc-L-Igl-OH.

[0111] Diagnosis, staging and treatment of pancreatic cancer. The most common method for classifying pancreatic cancer is whether it can be removed by surgery. and classifying pancreatic cancer into four categories based on where it has spread: Resectable, borderline resectable, locally advanced, or metastatic. The tumor may be confined to the pancreas or may extend beyond the pancreas. However, the tumor has not grown into any of the major arteries or veins in this area. There is no evidence of spread to outside areas. Using standard methods common in the medical industry today. As a result, only about 10% to 15% of patients are diagnosed at this stage. The field is characterized by the fact that, when first diagnosed, surgical removal is difficult or impossible. In some cases, chemotherapy and / or radiation therapy may initially shrink the tumor, and then Describes a tumor that can subsequently be removed with negative surgical margins. Negative surgical margins mean that no visible cancer cells are removed. Locally advanced pancreatic cancer remains limited to the area around the pancreas. It is present, but has grown into nearby arteries or veins or into nearby organs, It cannot be removed by surgery. However, locally advanced pancreatic cancer may spread to any distant part of the body. There is no indication that it has spread to other parts of the world. Approximately 35% to 40% of patients are diagnosed at this stage. It means that the tumor has spread beyond the normal area to other organs (e.g., the liver or distant areas of the abdomen). Using standard methods common in the medical community today, the diagnosis at this stage is , which accounts for approximately 45% to 55% of patients. Alternatively, the TNM method, which is commonly used in other cancers, A classification system may be used (however, this is not common in pancreatic cancer). is based on tumor size (T), lymph node spread (N) and metastasis (M).

[0112] Treatment options for pancreatic cancer include surgical procedures for partial or complete surgical removal of cancerous tissue. surgery (e.g., Whipple procedure, distal pancreatectomy, or total pancreatectomy), one or more chemotherapy regimens Administration of therapeutic agents and therapeutic radiation to affected tissues (e.g., conventional / standard stereotactic regional chemotherapies approved for the treatment of pancreatic cancer include the administration of SBRT. Therapeutic agents include, but are not limited to: capecitabine (Xelod) a), erlotinib (Tarceva), fluorouracil (5-FU), gemcitabine (Gemzar), irinotecan (Camptosar), leucovorin (Wellco vorin), nab-paclitaxel (Abraxane), nanoliposomal irino Tecan (nanoliposomal irinotecan) (Onivyde) and and oxaliplatin (Eloxatin).

[0113] Pancreatic cancer, when diagnosed early, preferably at or before the borderline resectable stage, is It is most effectively treated when diagnosed, preferably at a resectable stage. do. [Example]

[0114] The following examples are included to demonstrate embodiments of the present disclosure. The examples are presented as examples only and to aid one of ordinary skill in the art in using the present disclosure. It is not intended to otherwise limit the scope in any way. Those of ordinary skill in the art will be able to readily understand the disclosed Many modifications to the specific embodiments may be made without departing from the spirit and scope of the present disclosure. It should be recognized that the same or similar results can still be achieved without modifying the method.

[0115] Example 1: Mass spectrometry. Quantitative mass spectrometry (MS) analysis of human plasma samples was performed as previously described (Faca et al., PLoS Med. 5(6):e123, 2008). A pool of pancreatic cases in which blood was collected before the onset and diagnosis of the disease was included. 13 C-acrylamide isotope-labeled control pools were prepared before mixing these pools. The proteins were labeled with light acrylamide using a Workstation Class- Automation controlled by VP 7.4 (Shimadzu Corporation) The separation was performed using an on-line 2D-HPLC system. The fractions were lyophilized and then purified by reverse phase chromatography. The digestion was performed using NanoLC-1D (Exigent) and LTQ-Orbitrap (T The samples were analyzed by MS using a hermo mass spectrometer.

[0116] The obtained LC-MS / MS data was used for computational proteomics s Analysis System (CPAS) pipeline (Rauch et al. l., J. Proteome Res. 5(1):112-21, 2006) Custom scoring plugin with Comet for X!Tandem and Human Int International Protein Index (IPI) version 3.13. The search algorithm parameters were set to The mass tolerance was set for the precursor ions. In the case of the ion, it was 1.5 Da, and in the case of the fragment ion, it was 0.5 Da. 12 C ] Cysteine alkylation with acrylamide (+71.03657) as a fixed modification Settings, and then click 13 C] acrylamide (+3.01006) and oxidation of methionine (+15. 99491) were set as variable modifications. The identified peptides were het(Keller et al.,Anal.Chem.74(20):5383- 92, 2002) and proteins were further validated by ProteinProphet (N esvizhskii et al.,Anal.Chem.75(17):4646- Protein identification was assessed using ProteinProphet. The proteins were filtered with a 5% error rate based on the specified tool Q3. Quantitative information on the quality of each pair containing a cysteine residue identified by MS / MS was extracted. The peptides were quantified (Faca et al., J. Proteome Res. 5(8) :2009-18,2006). Minimum PeptideProphet score of 0.75 and only peptides with fractional delta masses up to 20 ppm were selected for quantification. 1 3 C] acrylamide-labeled peptide pair [ 12 [C] acrylamide-labeled peptide ratio log 2 The distributions are plotted on a linear scale, with the median centered at zero. All normalized peptide ratios for a given protein were averaged to calculate the overall protein ratio. I put it out.

[0117] This analysis yielded ProteinProphet scores of 0.8 or higher with an error rate of less than 5%. The core was used to identify 1,732 proteins, and the results were used for downstream analysis. Quantification of 395 proteins with at least two quantified peptides was also included.

[0118] Example 2: ELISA method. For all ELISA experiments, each sample was assayed in duplicate and analyzed by absorbance or chemiluminescence. Light was analyzed using a SpectraMax M5 microplate reader (Molecular Decoder). An internal control sample was run on every plate and plate To correct for inter-sample variability, each sample value was compared with an internal control in the same plate. Divided by the average value of

[0119] NPC2 Recombinant NPC2 (aa 20-151; SEQ ID NO: 3; UniProtKB: P6191 6) mouse monoclonal antibodies (#635 and #675) were produced and used in It was used in a cross-ELISA. [ka]

[0120] 96-well polystyrene plates (Corning, Canton, NY, USA) The capture antibody was 1 μg / mL of anti-NPC2 mouse monoclonal antibody (#635). followed by Reagent Diluent (R&D Systems Plasma samples were diluted 1:200 and serial dilutions of recombinant proteins were added. A standard curve was generated using a biotinylated anti-NPC2 mouse monoclonal antibody at a dilution of 1:4000. After washing, each well was treated with streptavidin- Incubation with HRP followed by color reagent and stop solution (R&D Syst The cells were incubated with 100 μg of PBS containing ...

[0121] Example 3: Blood sample set. Independent cohorts of blood samples were collected from PDAC cases (n=187), benign pancreatic disease patients (n=187), and All subjects were obtained from a pool consisting of 16 healthy controls (n=169) and 93 healthy individuals (n=93). The blood samples were submitted to the Institutional Review Board (UNR) rsity of Michigan Comprehensive Cancer C enter,Evanston Hospital,University of Ut ah,University of Texas MD Anderson Cance r Center and International Agency for Re All data were obtained after approval and informed consent from the National Cancer Institute.

[0122] Initial Discovery Set For the study using in-depth quantitative MS, six prediagnostic PDAC cases (gender, male; Median age, 66.5 years; range, 62–76 years) and 6 matched controls (gender, sex, A plasma pool was constructed from patients (male; median age: 67.0 years, range: 61-76 years). These samples were analyzed using the Carotene and Retinol Efficacy An average of 9.3 months (range, 8–12 months) after sample collection as part of the trial Patients subsequently diagnosed with stage IA (N=1), IB (N=2), and IIB (N=3) PDAC and from subjects matched for age, sex, and smoking history and followed up for 4 years. The data were collected from six cohorts from the same cohort who had not previously been diagnosed with cancer.

[0123] Triage Set The study consisted of 75 PDAC cases, 27 healthy controls, and 19 chronic pancreatitis cases. under the auspices of the Early Detection Research Network University of Michigan Comprehensive Can Plasma samples obtained from the cer center were used for initial validation and biomarker selection (Trial Used for the Urge Set.

[0124] Validation set 73 patients with early-stage PDAC, 60 healthy controls, and 60 patients with chronic pancreatitis An additional set of plasma samples from patients with pancreatic cysts and 14 patients with benign pancreatic cysts was performed. All chronic pancreatitis samples were used for serial biomarker validation and panel development. , collected in an elective setting in clinic in the absence of acute flare.

[0125] Validation set #1 from Evanston Hospital consisted of PDAs in stages IB-IIB. C cases (n=10), healthy controls (n=10), and chronic pancreatitis cases (n=10) Validation set #2 (University of Utah) consisted of early (IA to I IA) PDAC cases (n=42), healthy controls (n=50), and chronic pancreatitis cases ( n=50) and validation set #3 (University of Texas MD) Anderson Cancer Center) were treated with resectable PDAC cases (n= 21), and benign pancreatic cyst cases (n=14).

[0126] The demographics of these three validation sets are presented in Table 1.

[0127] Test Set A combined biopsy study consisting of 39 early-stage PDAC cases and 82 healthy controls An additional independent set of plasma samples for testing the oocyte marker panel was obtained from the Intern National Agency for Research on Cancer The demographics of this study set are presented in Table 2.

[0128] [Table 1]

[0129] [Table 2]

[0130] Example 4: Statistical Methods After imputing the lowest detection value for each assay, raw assay data were compared to values below the detection limit. The one-sided Wilcoxon rank sum test was used to compare PDAC cases with healthy controls. P values were calculated by comparing controls, chronic pancreatitis cases, and pancreatic cyst cases. The definition is to test the null hypothesis of AUC=0.50 against the alternative hypothesis of AUC>0.50. The objective was unilateral. Receiver operating characteristic (ROC) curve analysis was performed to compare PDAC cases. Biomarkers in distinguishing cases of chronic pancreatitis and pancreatic cysts from healthy controls Due to the small sample size of each set, validation sets #1, #2 and #3: Data are analyzed so that the mean is 0 and the standard deviation is 1 for healthy controls. Validation set #3 was combined for model development by standardization. The benign pancreatic cyst samples did not contain any steroids, suggesting that the mean values were the same as those of the chronic pancreatitis samples. Results were normalized to have a mean and standard deviation. Statistical analysis was performed using 4b and SAS version 9.3. All data were considered p<0.05. All analyses were considered statistically significant.

[0131] All possible combinations of seven validated biomarker candidates were investigated, and Akaike Based on the accuracy criterion (AIC), pancreatic cancer was distinguished from healthy controls, chronic pancreatitis, and pancreatic cysts. A logistic regression model was chosen to distinguish between the two groups. A total of 127 logistic regressions were performed. A regression model was fitted. Standard errors, confidence intervals, and p-values were calculated to account for the variability of the coefficients. Obtained by 1000-fold bootstrap. Biomarker panel and CA19-9 alone p-values for comparing the AUC (panel ) = AUC(CA19-9) vs. alternative AUC(panel) > AUC(CA19-9) Refer to the instructions. The likelihood ratio test was also applied to compare the fitness of the biomarker panel with CA19-9 alone. The LeaveMOut cross-validation technique was applied to validate the obtained logistic regression model. The data was split into a training set and a test set, and these sets corresponded to 2 / 3 and 1 / 3 of the original data, respectively. Such a splitting scheme was repeated 1000 times, and the model was validated by averaging the 1000 AUCs obtained from the test set. A modified planned covariance matrix was applied to construct a logistic regression model with an OR rule that can distinguish pancreatic cancer from patients with chronic pancreatitis and benign pancreatic cysts: [I(Ca19-9>=a)Ca19-9 I(Ca19-9>=a)I(Ca1 * I(Ca19-9>=a)I(Ca1 9-9<a)TIMP1 * I(Ca19-9<a)LRG1 * I(Ca19-9<a)C A19-9 * I(Ca19-9<a)]. All possible values of the CA19-9 threshold "a" were scanned, and the highest possible AUC was achieved by 1000-fold bootstrap. Boot strap was used to create a prediction score using the measurements not initially selected, and the AUC was evaluated. This procedure was repeated 1000 times, and two-sided p-values were calculated for the 1000 AUCs obtained. The highest AUC was obtained at "a" = 1.6. To avoid overfitting in the development of the test set of the logistic regression model that includes the three biomarkers TIMP1, LRG1, CA19-9 along with covariates (represented by recruitment center, sex, age, smoking status, and alcohol consumption), a two-step strategy was followed. First

[0132] covariates by fitting a logistic regression model including only the covariates. A covariate-based score is then created, and this covariate-based score is then used to compare the three variables as a single covariate. The biomarkers were added to the logistic regression model.

[0133] Example 5: Selection of candidate biomarker panels. The NPC2 assay described above was utilized in this study. Potential biomarker panels are listed in Table 3.

[0134] [Table 3]

[0135] Example 6: Biomarker panel composition discovery. triage The flow diagram for this study is shown in Figure 1. Briefly, 18 potential biomarkers were The pool was trimmed by screening against a set of 12 triases. Biomarker levels were statistically significantly higher in PDAC compared to healthy controls The results were significantly higher than those of the control group, with area under the curve (AUC) > 0.60 and p < 0.05 (Wil Coxon rank sum test) (Figure 2A). P2, LRG1, CA19-9, REG3A, COL18A1, TIMP1 and TNF RSF1A) levels were also statistically significantly higher in PDAC cases compared with chronic pancreatitis cases. The results were high (p<0.05, Wilcoxon rank sum test) and had an AUC of >0.60 (Figure 2B). These seven biomarker candidates were compared for validation sets #1, #2, and #3. A triase panel was selected for further evaluation.

[0136] verification These seven candidate biomarkers in the triage panel were then analyzed using the methods described above. All seven validation sets selected in the triage set were analyzed. The AUC values of biomarkers were compared with the corresponding controls in validation sets #1, #2 and #3. In comparison, plasma levels were consistently elevated in PDAC patients (Table 4, Table 5 and Table 6). These seven markers in the comparison of PDAC vs. chronic pancreatitis cases in validation set #2. The AUC of Car (excluding IGFBP2) was significantly higher in both validation sets #1 and #2 than in healthy controls. The discrimination of PDAC cases from controls and chronic pancreatitis cases was >0.60. and four biomarkers (CA19-9, TIMP1, LRG1 and IGFBP2). In validation set #3, plasma samples from PDAC cases were significantly higher than those from benign pancreatic cyst cases. This resulted in an AUC of <0.60 in the pool (Table 6).

[0137] [Table 4]

[0138] [Table 5]

[0139] [Table 6]

[0140] Panel Construction Validation Sets #1 and #2 to develop a biomarker panel for early-stage PDAC The results of #1 and #2 were normalized and combined. In the combined validation set, PDAC disease The levels of all seven biomarkers in the study were significantly higher in healthy controls and patients with benign pancreatic disease. The statistically significant difference was observed in the cases of chronic pancreatitis and benign pancreatic cysts (combination of chronic pancreatitis and benign pancreatic cysts). The degree of variability was high (AUC>0.60; p<0.05, Wilcoxon rank sum test) (Table 7) Next, we developed a biomarker profile for early-stage PDAC based on a logistic regression model. developed the Nel.

[0141] The resulting regression model is logit(p)=-1.97+1.7005×logTIMP1+0.93856×l ogLRG1 + 0.60639 × logCA19.9 where p denotes the probability of a case being present in a given sample. is a normal logistic regression model with a logit link function. (binary disease status) plays a role in the response and The algorithm for fitting such a regression model is The algorithm is standard and is described in detail in standard textbooks on generalized linear models. It is based on an iterative reweighting procedure (McCullogh et al., Generaliza ed Linear and Mixed Models(2008);Wiley S series in Probability and Statistics,John Wiley & Sons, Inc., Hoboken, New Jersey). Although this standard approach is applied to model fitting, , but cannot provide inferences on the underlying AUC. Provides p-values and confidence intervals that refer to the AUC. To do this, we use a variability test for each bootstrap sample to take into account the variability of the estimated coefficients. We employed a bootstrap scheme in which the coefficients were re-estimated (1000 times in total) within the model. Ta.

[0142] The LeaveMOut cross-validation technique was applied to validate the obtained logistic regression model. The resulting panel demonstrated a 0.949 AUC (95% CI) of (0.917-0.981) and cross-validated association mean of 0.936 The mean AUC for TIMP1, LRG1, and CA19-9 was The AUC of -9 alone (AUC (95% CI) = 0.882 (0.809-0.956)) The difference was statistically significant compared to the control group (p = 0.003, bootstrap; p < 0 .001, likelihood ratio test; Table 8 and Figure 3A). This panel shows the 95% and 99% likelihood ratios, respectively. % specificity of 0.849 and 0.658, respectively, whereas CA19-9 alone The sensitivity at 95% and 99% specificity was 0.726 and 0.411, respectively. A model based on the same biomarker combination (TIMP1, LRG1, and CA19-9) The delta was trained on validation set #2 and tested with a fixed coefficient on validation set #1 (p=0. 04, bootstrap on training set; p=0.02, bootstrap on test set In the case of PDAC cases compared with healthy controls (Table 9), CA19-9 alone We also observed a significant improvement over the German study. This result was consistent with the validation set, where tumor size was available. In study #2, panel-based biomarker scores demonstrated statistical significance for tumor size. Without being limited by theory, this is because the combination of biomarkers This suggests the ability to detect tumors of small size (Figure 4).

[0143] A logistic regression model based on the same biomarker combination (TIMP1, LRG1, and CA19-9) was used. A logistic regression model was developed to distinguish PDAC from benign pancreatic disease cases (AUC( 95%CI) = 0.846 (0.781-0.911) and cross-validation associated mean AUC = 0.830, Table 8). A linear regression model based on the "OR" rule was performed to compare CA19-9 alone or Any combination of all three markers showed a significant difference between PDAC and benign pancreatic disease cases. We also investigated whether it would be possible to distinguish between TIMP1, LRG1, and CA19. The combination of -9 "OR" rules had an AUC(95%) of 0.890 (0.802-0.978). %CI), which was significantly lower than CA19-9 alone (AUC(95%CI)=0.831 (0.754-0.907) was statistically significantly greater than that (p<0.00 1 bootstrap; p<0.001, likelihood ratio test; Table 8 and Figure 3B).

[0144] Regression models for the discrimination of PDAC from benign pancreatic disease are logit(p)=-1.2497+0.50306×logTIMP1+0.2535 5×logLRG1+0.51564×logCA19.9 where log refers to the base 2 logarithm. This defines the logit link function as By utilizing a binary disease state as the response and a marker as a covariate, , obtained by fitting a normal logistic regression model. The algorithm for fitting the regression model is standard and is a generalized linear model. See standard texts on the subject (McCullogh et al., supra). Based on an iterative reweighting procedure described in detail. CA19-9 alone and the three-marker panel The trade-off between O and O is examined based on the decision values varied by the grid search. The R rule was further investigated: CA19-9 alone or across all three markers. We considered a normal logistic regression model with the contribution of the design matrix. Based on a fine grid of threshold points, all models are calculated for all points in the grid. We extracted exemplary AUCs that can be derived after iteratively fitting the model.

[0145] This panel yielded a sensitivity of 0.452 at 95% specificity, which is significantly higher than that of CA19-9 alone. This represents an improvement over the sensitivity of 0.288 at 95% specificity for TIMP1, LRG1 and The combination of CA19-9 and CA19-9 with the "OR" rule had an AUC( 95% CI), which is high when applied to comparisons between PDAC patients and healthy controls Diagnostic accuracy (p vs. CA19-9: p<0.001 bootstrap; p<0. 001, likelihood ratio test; Table 8).

[0146] Odds ratios were estimated at optimal cutoff points based on the Youden index. For the model regarding AC cases versus healthy controls, log(odds ratio) has a sensitivity of 0. The cutoff point was 4.67 (95% CI = 0.849) with a specificity of 0.950. 3.29-6.05). In this case, the log(odds ratio) is a coefficient with a sensitivity of 0.863 and a specificity of 0.757. At the off-point, the mean was 2.98 (95% CI = 2.04-3.91).

[0147] [Table 7]

[0148] [Table 8]

[0149] [Table 9]

[0150] Example 7: Evaluation of a biomarker panel. Using the test set, three biomarkers, TIMP1, LRG1 and CA19- Further blinded validation of the panel of 9 was performed. Levels of all three biomarkers were significantly higher in healthy individuals. The AUC (95% CI) was significantly higher in PDAC cases than in healthy controls. was 0.821 (0.736-0.906) for CA19-9 and 0.821 (0.736-0.906) for TIMP1. The values for LRG1 and LRG2 are 0.730 (0.626-0.834) and 0.832 (0.832-0.834), respectively. The linear combination of these three markers was The AUC (95% CI) was 0.903 (0.838-0.967), which is The AUC was statistically significantly greater than that of 19-9 alone (p=0.001, p<0.001, likelihood ratio test; Table 11). 1, CA19-9 and covariates (recruitment center, sex, age, smoking status, and alcohol A linear combination of the two variables (expressed as consumption) yielded an AUC (95% CI) of 0.929 (0.878 ~0.980, which is comparable to the CA19-9 and covariate combination alone (AUC(95 %CI) = 0.848 (0.778-0.920) showing a statistically significant improvement p=0.01, bootstrap; p<0.001, likelihood ratio test; Table 11). The inclusion of the 3-biomarker panel resulted in a statistically significant improvement in performance compared to the 3-biomarker panel alone. (p = 0.03, bootstrap; p = 0.004, likelihood ratio test; Table 11).

[0151] Notably, in the combined validation set for PDAC versus healthy controls Logistic regression of CA19-9, TIMP1, and LRG1 with fixed coefficients developed The regression model yielded an AUC of 0.887, similarly demonstrating consistent performance compared to CA19-9 alone. The statistically significant improvement was observed (p = 0.008, likelihood ratio test; Table 10 and Figure 5). The assay yielded sensitivities of 0.667 and 0.410 at specificities of 95% and 99%, respectively. However, the sensitivity at 95% and 99% specificity for CA19-9 alone was 0. The optimal cutoff points based on the Youden index were 0.538 and 0.462. The log-transformed odds ratio for the cutoff was 0.872 for sensitivity and 0.780 for specificity. The mean score was 3.19 (95% CI = 2.11-4.26).

[0152] Example 8: Specificity and sensitivity of regression models across a range of diagnostic scores. Various methods for detecting, quantitating, and analyzing biomarkers may involve the use of various reagents. Different methods or assays may produce different results that may require modification of this regression model. Those skilled in the art will recognize that various assays may be used, e.g. Furthermore, duplicate reactions in duplicate assays of the same sample can produce results expressed in units. However, at least three biomarkers, TIMP1 and TIMP2, can produce different results. The combined detection, quantification and analysis of LRG1 and CA19-9 is disclosed herein. When incorporated into the regression model shown, it results in a definitive diagnosis of PDAC.

[0153] Regarding each specific assay used for the detection, quantification, and analysis of the three biomarkers The range of results reported depends in part on the degree of sensitivity or specificity of the PDA obtained. C Prediction score range (Table 12; favorable cupping based on Youden Index) The toff is 0.8805 with a specificity of 0.95 and a sensitivity of 0.8493). The regression model used to generate the PDAC prediction score was used to test the markers As will be appreciated by those skilled in the art, various A suitable assay may target different epitopes of the three biomarkers or may utilize different Therefore, the PDAC predictive score can be generated using the affinity and sensitivity of the target gene. The regression model algorithms used can be modified to account for these assay variations.

[0154] Example 9: Assay of samples and diagnosis of PDAC patients. In one example, a diagnostic test for PDAC based on a three biomarker panel disclosed herein is provided. Blood samples (or other fluid or tissue biopsies) from patients screened ) and assessed by ELISA (or other assay) to determine the presence of TIMP1 in this patient. , LRG1 and CA19-9 levels were quantified. , ELISA; Table 3) consider the normalized values of at least these biomarkers, e.g. For example, TIMP1 = 0.6528 ng / mL; LRG1 = 2.0498 ng / mL; and The raw assay data was then log2 Transform and calculate the mean and standard deviation for the healthy samples in each cohort. , normalize the data so that the healthy samples have a mean of 0 and a standard deviation of 1 (Re ad j -mean healthy ) / (std healthy ), where j is the jth This is a sample.

[0155] The following regression model: logit(p)=-1.97+1.7005×logTIMP1+0.93856×l ogLRG1 + 0.60639 × logCA19.9 When analyzed using the NIH 2000 Average Score (ASC) 2.0001, the patient had a combined score of 2.1653. Considering the preferred cutoff for both specificity and sensitivity considerations, (Table 12), patients with such a combined score almost certainly have PDAC. and, as a result, other modalities discussed herein and known to those skilled in the art. The present invention is directed to the follow-up testing and treatment of PDAC using the regression model described herein. Using the DISCOVERY, the more positive the combined PDAC prediction score, the better a patient will be able to Conversely, a negative combined PDAC prediction score The more likely the patient is to not have PDAC.

[0156] In contrast, another example would be a biomarker TIMP that takes into account the specific assay used. 1. The normalized values of LRG1 and CA19-9 are, for example, TIMP1=-2.0370n g / mL; LRG1=-1.5792ng / mL; and CA19-9=1.0712U When analyzed using the same regression model as above, such patients , would have a combined score of -6.2666. Both specificity and sensitivity Considering the preferred cutoffs for consideration of (Table 12), such combined Patients with a score almost certainly do not have PDAC and therefore should not be subsequently screened for any Further testing may or may not be required based on the severity of other clinical conditions.

[0157] [Table 10]

[0158] [Table 11]

[0159] [Table 12]

[0160] [Table 13]

[0161] [Table 14]

[0162] Example 10: Plasma metabolite and protein markers for the detection of early stage pancreatic cancer Combined panels for resectable pancreatic ductal adenocarcinoma (PDAC) using an untargeted metabolomic approach A plasma-derived metabolite biomarker panel was developed using liquid chromatography / mass spectrometry. A multi-assay metabolomics approach using genomic analysis was performed on 20 patients (10 early and 10 late-stage PDAC cases and 20 matched controls (10 healthy controls) 10 subjects with chronic pancreatitis) to identify candidates for PDAC. Co-metabolite markers were identified and candidate markers were analyzed in 9 cases of PDAC and 50 cases of benign pancreatic cancer. The study was narrowed based on a separate “validation” cohort of subjects with BPD. 39 An independent cohort of 82 resectable PDAC cases and 82 matched controls was conducted. A blinded validation study was conducted in a randomized controlled trial. Five metabolites, including: acetylspermidine, diacetylspermine, lysophosphatase lysophosphatidylcholine (18:0), lysophosphatidylcholine (20:3) and indole derivatives Conductors were identified in the discovery and "validation" cohorts. A metabolite panel was developed based on the data and analyzed in a combined discovery and "validation" cohort. Regarding the ability of this metabolite panel to distinguish PDAC from healthy controls The resulting panel had a mean of 0.90 (95% CI: 0.818-0.989). The blinded validation of this metabolite panel included independent validation controls. The AUC was 0.89 (95% CI: 0.828-0.956) in the study. This metabolite marker and the protein marker (CA19- 9, TIMP1 and LRG1) in the validation cohort. This resulted in an AUC of 0.92, which was significantly higher than the AUC of this protein panel alone (AUC = 0.86; p-value :0.024), which is statistically significantly greater than the 3-protein marker panel This highlights the complementary nature of this metabolite panel when combined with

[0163] Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality in both men and women in the United States. The overall 5-year survival rate is only about 8%. The diagnosis is uncommon, and the majority of patients (approximately 85%) usually present with locally advanced disease or It is an incidental presentation of metastatic disease.

[0164] Currently, no clinical markers exhibit desirable performance characteristics for early-stage PDAC in asymptomatic individuals. The current use of CA19-9 as a screening biomarker is based on this Variable accuracy of A19-9, decreased performance in prediagnostic stages of disease, and fucosyltransferase activity It is limited by its undetectable nature in approximately 10% of subjects with spherase deficiency. The result is higher sensitivity and specificity for reliable detection of low-abundance PDAC in asymptomatic individuals. There is a great need for additional markers that collectively indicate the degree of abnormality. A biomarker based on HIV-1 would be ideal and relatively non-invasive for early detection of disease. It represents a cost-effective method for

[0165] Recently, protein-based assays for the detection of early PDAC have been developed, which may complement CA19-9. The development and subsequent validation of a biomarker panel for CA19 was performed. Although this was an improvement over the -9 alone, there was still room for improvement. To enable the development of optimal biomarker combination models, various types of metabolites, etc. The relative contribution of these biomarkers needs to be examined.

[0166] In this study, we applied an untargeted metabolomics approach to identify plasma-derived A metabolite biomarker panel was developed. The fixed biomarker panel was then In addition to comparison against a panel of previously identified proteins, 39 patients with resectable PDAC were The study was conducted blindly in an independent study cohort consisting of 82 patients and 82 matched healthy controls. This metabolite panel was shown to distinguish PDAC cases from subjects diagnosed with benign pancreatic cysts. The performance of the

[0167] Study population All human blood samples were submitted to the Institutional Review Board After approval and informed consent, 20 Patients with PDAC (including 10 early-stage PDAC and 10 late-stage PDAC) Plasma samples from 10 healthy controls and 10 patients with chronic pancreatitis All chronic pancreatitis samples were obtained from Evanston Hospital (discovery set). Samples were collected in an elective clinic setting in the absence of acute flare. 50 hypoplastic dysplasias Patients with grade 1 pancreatic cysts and 9 cases of invasive IPMN (5 cases of early stage adenocarcinoma and 4 cases of invasive IPMN) A study of patients with advanced adenocarcinoma of the thyroid gland (T2G) at Indiana University School of Medicine Plasma samples obtained from the School of Medicine were analyzed for sequential selection and characterization of biomarkers. All patients underwent surgical resection of cystic lesions. Plasma samples were collected before surgery. Dysplasia grade was assessed by histopathology after surgical resection. The 39 cases of early stage PDAC and 82 cases of healthy controls were randomly identified and determined according to WHO criteria. Further independent studies are underway to test combined biomarker panels consisting of The isolated plasma samples were submitted to the International Agency for Research 102 hypoplastic gray matter samples were obtained from the Archives on Cancer (Study Set #1). 12 patients with pancreatic cysts, 12 patients with resectable invasive IPMN, and 12 patients with IPM Indiana University, consisting of 8 resectable PDAC patients with N Another sample set from the University School of Medicine was used as the test set. 2. Study flow diagram and patient data for the validation and test sets. Clinical characteristics are presented in Figure 6 and Tables 13 and 14.

[0168] [Table 15]

[0169] [Table 16]

[0170] [Table 17]

[0171] [Table 18]

[0172] Metabolomic experiments of cell lines PDAC cell lines (CFPAC, MiaPaCa, SU8686, BxPC3, CAPA N2, PANC03.27, and SW1999) were cultured in RPMI-16 containing 10% FBS. The identity of each cell line was confirmed using the PowerPlex 1.2 kit (Pro Use the ATP-mega (probe) to perform short tandem reactions during the preparation of mRNA and total protein lysates. This was confirmed by DNA fingerprinting using the DNA fingerprinting method. Results were compared to reference fingerprints maintained by the primary source of the cell line. The cells were seeded in a 6 cm dish (Thermo Scientific). , 24 hours after initial seeding, the culture density reached 70% (50-80%). The cell lysate was washed twice with pre-chilled 0.9% NaCl followed by pre-chilled extraction buffer. Quench by adding 1 mL of buffer (3:1 isopropanol:ultrapure water) and remove cell culture medium. The cells were then scraped in the extraction solvent using a 25 cm cell scraper (Sarstedt). The cells were scraped off using a vortex and transferred to a 1.5 mL Eppendorf tube. After mixing, the extracted cell lysate was centrifuged at 2,000 x g for 10 minutes at 4°C. Then, 1 mL of the supernatant containing the extracted metabolites was placed in a 1.5 mL Eppendorf tube. The mixture was transferred to a tube and stored at −20° C. until required for metabolomic analysis.

[0173] Exometabolome experiments Cells were cultured in 12-well dishes (Costar) in RPMI 1640 + 10% Grown in 1 ml of FBS, the cells reached 70% (50-8%) of cell viability 24 hours after initial seeding. On the day of the experiment, the cells were cultured in 5 mM glucose and 0.5 mM glycerol. 500 μL of serum-free RPMI containing glutamin (Fisher Scientific) The cells were then washed twice with serum-free PBS containing 5 mM glucose and 0.5 mM glutamine. RPMI (300 μL) was added to each well and the cells were incubated. After the incubation period (1, 2, 4 and 6 hours), 250 μL of conditioned medium was collected. For the baseline (T0), 300 μL of medium was added and immediately 250 μL was withdrawn. All time points were performed in triplicate or quadruplicate. Blank samples containing medium only were included, and T0 and T1 were The 6-hour sample was used to count cells for data normalization. Once all media samples were collected, the tubes were centrifuged at 2000 x g for 10 minutes. Centrifuge to remove residual debris, transfer the supernatant to a 1.5 mL Eppendorf tube, and They were stored at −80°C until used for bolomics analysis.

[0174] Primary metabolites and biogenic amines LCMS-grade 96-well microplates (Eppendorf) were 30 μL of methanol (ThermoFisher) was added to the pre-aliquoted EDTA plasma ( Serum metabolites were extracted from 10 μL of the plate. The plate was heat sealed and heated at 750 rpm for 5 minutes. The supernatant was vortexed and centrifuged at 2000 x g for 10 minutes at room temperature. (10 μL) was carefully transferred to a 96-well plate, leaving behind the precipitated proteins. The supernatant was further diluted with 10 μL of 100 mM ammonium formate (pH 3). For liquid chromatography (HILIC) analysis, this sample was purified using LCMS-grade Dilute with 60 μL of acetonitrile (ThermoFisher) and add 10 mL of C18 for analysis. The sample was diluted with 60 μL of water (GenPure ultrapure water system, ThermoFisher). Each sample solution was diluted and placed in a 384-well microplate (Eppendorf) for LCMS analysis. Pendorf).

[0175] For cell lysates, add 100 µL (3:1 isopropanol:ultrapure water) to two 300 ml plates. The solution was dispensed into 96-well plates (Eppendorf) in μL and evaporated to dryness under vacuum. The sample was then reconstituted as follows: for HILIC assay, dried The sample was diluted with ACN (Fisher Scientific): 100 mM ammonium formate. The solution was dissolved in 65 μL of sodium chloride (pH 3) (9:1) and dried for the reversed-phase C18 assay. The sample was dissolved in 65 μL of H2O:100 mM ammonium formate (pH 3) (9:1). The sample was then spun down to remove any insoluble material and analyzed by LCMS. Transferred to 384-well plates for high-throughput analysis.

[0176] Thaw the frozen medium sample on ice and 30 μl of the sample was added to 100 mM ammonium formate (pH 3 0.0) and transferred to a 96-well microplate (Eppendorf) containing 30 μL of the solution. The microplate was heat sealed, vortexed at 750 rpm for 5 minutes, and stored at room temperature. The mixture was centrifuged at 2000 x g for 10 minutes. For HILIC analysis, 25 μL of sample was added to 75 μL of acetonitrile. Transfer the samples for C18 analysis to a new 96-well plate containing 75 μL of water (G enPure Ultrapure Water System (ThermoFisher) Each sample solution was transferred to a 384-well microplate for LCMS analysis. (Eppendorf).

[0177] For each batch, samples were randomized and matrix-matched reference quality controls were Role and batch specific pooled quality controls were included.

[0178] Complex lipids LCMS-grade 96-well microplates (Eppendorf) were 30 μL of 2-propanol (ThermoFisher) was added to the pre-aliquoted EDTA Plasma samples (10 μL) were extracted. The plates were heat sealed and heated at 750 rpm for 5 minutes. The mixture was vortexed briefly and centrifuged at 2000 x g for 10 minutes at room temperature. 10 μL) was carefully transferred to a 96-well plate, leaving behind the precipitated proteins. 1:3:2 100 mM ammonium formate (pH 3) (Fischer Scientific Further dilution with 90 μL of acetonitrile:2-propanol was performed and analyzed by LCMS. Transfer the liquid to a 384-well microplate (Eppendorf) for analysis. Ta.

[0179] For cell lysates, 300 μL of extracted cell lysate was added to a 96-well plate. Add 10 μL of the supernatant of the lysed metabolites (3:1 isopropanol:ultrapure water) to a 1:3:2 100 mL solution. 1 mM ammonium formate (pH 3):acetonitrile:2-propanol (Fisher Dilute with 90 μL of the Scientific (Scientific) 384 wells for analysis using LCMS. The mixture was transferred to a microplate (Eppendorf).

[0180] For each batch, samples were randomized and matrix-matched reference quality controls were Role and batch specific pooled quality controls were included.

[0181] Non-targeted analysis of primary metabolites and biogenic amines 2D column coupled to a Xevo G2-XS quadrupole time-of-flight (qTOF) mass spectrometer Waters Acquity™ U with regenerative configurations (I-class and H-class) Untargeted metabolomics analysis was performed on a PLC system. ity(TM) UPLC BEH amide, 100Å, 1.7μm 2.1×100mm, Waters Corporation, Milford, USA) and C18( Acquity(TM) UPLC HSS T3, 100Å, 1.8μm, 2.1×10 0 mm, Water Corporation, Milford, USA) column. Chromatographic separation was carried out using

[0182] The quaternary solvent mobile phase consisted of (A) 0.1% formic acid in water, (B) 0.1% in acetonitrile, (D) formic acid and (E) 100 mM ammonium formate (pH 3). Separation was performed using a gradient profile: 95% B and 5% D for HILIC separation. The starting gradient was 70% A, 25% B, and 5% D over 5 min at a flow rate of 0.4 mL / min. A linear increase was followed by a 1-minute isocratic gradient at 100% A at a flow rate of 0.4 mL / min. For the C18 separation, the chromatographic gradient was as follows: condition, linear increase to the final condition of 100% A, 5% A, 95% B, then 1 min Isocratic gradient of 95% B, 5% D.

[0183] A binary pump was used for column regeneration and equilibration. The solvent mobile phase was (A1) 10 0 mM ammonium formate (pH 3), (A2) 0.1% formic acid in 2-propanol, and (B1) 0.1% formic acid in acetonitrile. The HILIC column was run for 5 min. Strip using 90% A2, then 1 mL / min at a flow rate of 0.3 mL / min. Equilibrated with 95% A1, 5% B1 for 2 min. The reversed-phase C18 column was regenerated using 5% A1, 95% B1 for 5 min. The column was equilibrated using

[0184] Untargeted analysis of complex lipids Xevo G2-XS quadrupole for lipidomic assays A 2D column regeneration configuration (I-class and H-class) coupled to a time-of-flight (qTOF) mass spectrometer Untargeted metabolic analysis was performed on a Waters Acquity™ UPLC system with The romics analysis was performed on a C18 (Acquity™ UPLC HSS) at 55°C. T3, 100Å, 1.8μm, 2.1×100mm, Water Corporatio Chromatographic separation was performed using a PEG-400 column (Milford, USA). The mobile phases were (A) water, (B) acetonitrile, (C) 2-propanol, and (D) 500 mM ammonium formate (pH 3). 20% A, 30% B, 49% C and The starting elution gradient of 1% D increased linearly to 10% B, 89% C, and 1% D over 5.5 minutes. followed by an isocratic elution of 10% B, 89% C, and 1% D over 1.5 min. This was followed by column equilibration with the initial conditions for 1 min.

[0185] Mass spectrometry data acquisition Mass spectrometry data were analyzed for metabolites in the range of 50–1200 Da for primary metabolites and for complex metabolites. Sensitivity within 100-2000 Da for complex lipids, positive and negative electrospray For electrospray acquisition, the capillary voltage was set to Set the sample cone voltage to 1.5 kV (positive), 3.0 kV (negative), and 30 V. source temperature, 50 L / hr cone gas flow, and 0.5 second scan in continuous mode. The desolvation gas flow rate was set at 800 L / h with a gap between the lock spray and the skimmer. Leucine enkephalin for cancer; 556.2771 Da (positive) and 554.26 15 Da (negative) was performed in 0.5 min. Unless otherwise specified, the injection volume of each sample was 3 The automatic gain control of the instrument was used to optimize instrument sensitivity over the sample acquisition period. The acquisition was performed as per your request.

[0186] Pooled quality control samples were analyzed after a defined number of samples to assess replicate accuracy. The pooled quality control was performed to evaluate the effect of injection order on LOESS correction. Further data was obtained using the MSe function on the sample.

[0187] Data Processing Peak picking and retention time alignment of LC-MS and MSe data This was performed using Progenesis QI (Nonlinear, Waters). Data processing and peak annotation were performed using an in-house automated pipeline. Accurate quality control was achieved using a customized library created from a trusted standard. By matching the amount and retention time, and / or NIST MSMS, Lipid Experimental tandem mass analysis versus dBlast or HMDB v3 theoretical fragmentation The annotation was determined by matching the analytical data. Drift in the injection order was compensated for. To ensure consistency, quality control samples were collected every 10 injections throughout the run sequence. Each feature was normalized using data from replicate injections of the same protein, as previously described (1). Locally Weighted Scatterplot Smoothing ( The measurement data was smoothed using QC-RLSC (Loess) signal correction. Detected features exhibiting a relative standard deviation (RSD) of less than 30 in the control samples Only the data were considered for further statistical analysis. , one representative unique annotated feature with multiple adjuncts or repeats of the acquisition mode Collapsed features. Features were classified by replicate accuracy (RSD<30), intensity, and theoretical isotope distribution. For each analytical batch for a given analyte, Report values as a ratio to the median of historical quality control reference samples run simultaneously. do.

[0188] Enzyme-linked immunosorbent assay Plasma protein concentrations of CA19-9, LRG1, and TIMP1 were measured as previously described. All ELISA experiments were performed using the same method (Capello et al., 2017). Each sample was assayed in duplicate and absorbance or chemiluminescence was measured using SpectraMatrix. x M5 microplate reader (Molecular Devices, Sunny An internal control sample was run on all plates and the samples were analyzed. Each value of the pool was divided by the mean value of the internal controls on the same plate to correct for interpolation variability. Ta.

[0189] Gene Expression Data and Networks Gene expression for the Badea dataset was downloaded from the oncomine database. I used SiteScope to visualize the network.

[0190] statistical analysis Receiver operating characteristic (ROC) curve analysis was performed to compare the mean and mean values of healthy controls and benign pancreatic disease. Biomarkers in distinguishing PDAC cases from subjects diagnosed with chronic pancreatitis or pancreatic cysts The performance of the system was evaluated.

[0191] The AUC corresponding to the individual performance of all biomarkers was calculated using receiver operating characteristic curves (ROC The individual performance of each biomarker is estimated using the area under the empirical estimator The standard errors (SE) and corresponding 95% confidence intervals presented for the control and reconstructed with separate replacement on a bootstrap sample of 1000 affected cases. Sampling was based on a bootstrap procedure. Marker LPC (18: 0), LPC(20:3) and indole-3-lactate, these markers indicates higher measurements for the control compared to measurements corresponding to the cancer-associated samples Since there is a tendency for the trend to be reversed, we took care to consider the reverse direction. The model was based on a logistic regression model using a log function. By using empirical estimators of binding, the estimated AU of the proposed metabolite panel C (0.9034) was derived. Metabolism based on AUC (0.8180-0.9889) The 95% confidence intervals reported for the product panel were based on the underlying logistic regression model This takes into account the fact that the coefficients of were estimated, and therefore bootstrapped with 1000 iterations. Using traps ensures that all bootstrap replicates provide adequate inference. The coefficients of the model are re-estimated to show the variability. Panel refers to a combination of panels (a panel for proteins and a panel for metabolites). The model combines these two panels into two composite models, taking into account the fixed coefficients of each. Used as a composite marker (one for proteins and one for metabolites) This hyperpanel has been developed by the inventors, taking into account the logit link function. These two underlying composite markers were combined using a logistic regression model. It was developed by combining

[0192] result Identification of metabolite biomarkers for pancreatic cancer Untargeted metabolomic analysis was performed on 20 PDAC cases (10 early and 10 late). stage) and 20 matched controls (10 healthy subjects and 10 patients with chronic pancreatitis) This was performed in a discovery cohort (Set #1) consisting of subjects with CP (Figure 6). Based on a reasonable ROC AUC (two-tailed Wilcoxon rank sum test < 0.05), candidate biomarkers were selected. Markers were initially selected, resulting in 91 metabolites (Table 15). To narrow this down, metabolomic analysis was performed on 9 cases of PDAC (5 early and 4 late). ) and 50 independent subjects with benign pancreatic disease (BPD) (benign pancreatic cysts). Of the 91 original features, 16 were , retained a significant AUC and showed the same direction of relative change (increase / decrease) as observed in Set 1. The candidate metabolites were further refined to identify (1) early stage PDAC and Metabolites that showed similar levels between subjects with and without CP (one-sided Mann-Whitney y U test p<0.1), and (2) metabolites that differed between CP and healthy controls. (One-tailed Mann-Whitney U test p<0.1) were excluded (Table 16). For lipid species, lipid classification is used to reduce nonspecificity due to external factors such as dietary patterns. Highlighted were lipids that exhibit uniformity of performance characteristics across the entire class (i.e., within a given lipid class). >80% of individual lipids detected were concordantly increased / decreased in cases compared with controls A total of five metabolites that met the above criteria were selected (Figure 7). The metabolites of this compound are (N1 / N8)-acetylspermidine (AcSperm), diacetyl Spermine (DAS), lysophosphatidylcholine (LPC) (18:0), LPC (2 0:3) and indole derivatives (Figure 8 and Table 17).

[0193] [Table 19]

[0194] [Table 20]

[0195] [Table 21]

[0196] [Table 22]

[0197] [Table 23]

[0198] Table 24

[0199] Table 25

[0200] Table 26

[0201] Table 27

[0202] Table 28

[0203] Table 29

[0204]

Table 30

[0205] Table 31

[0206] Table 32

[0207] Table 33

[0208] Next, we developed a biomarker panel for PDAC based on a logistic regression model. PDAC cases (n=29) from sets #1 and #2 were combined and included in set #1. The results were evaluated for healthy subjects (n = 10) from #1 (Figure 7). The estimated coefficients obtained by the combined logistic regression model are shown in Table 18. The individual performance of the 5-metabolite markers on the dataset is shown in Table 17. In comparison with healthy subjects, the obtained AcSperm+DAS+LPC(18:0)+L The panel of PC(20:3) + indole derivatives had a mean score of 0.90 (95% CI = 0.81 This resulted in an AUC of 0.8 (0.989), demonstrating a sensitivity of 69% with a specificity of 99% (Figure 9A). Use of a metabolite panel for differentiation of PDAC from PD (chronic pancreatitis and low-grade cysts) The diagnostic efficacy was 0.69 (95% CI = 0.557-0.69) with a sensitivity of 41% and a specificity of 95%. The maximum AUC achieved was obtained with the indole derivative alone. The results were as follows: AUC = 0.833 (Figure 9B).

[0209] Metabolite biomarkers in two independent sets of resectable PDAC plasma samples -Panel testing Blinded validation of five metabolites individually and as a panel in 39 patients with resectable PDAC The study was performed on an independent set of plasma samples consisting of 82 patients and 82 matched healthy controls. All five biomarkers were in the range of 0.73 to 0.84. The individual AUCs of All five metabolites were significantly elevated in the initial cohort (one-sided p<0.001) (Table 19). The observed changes were consistent with the increase / decrease observed for the 5-metabolite panel. The logistic regression model for this was 0.89 (95% CI = 0.828-0.956). This yielded an AUC of 0.01, indicating a sensitivity of 6% at a specificity of 95% (Figure 10 and Table 19).

[0210] [Table 34]

[0211] [Table 35]

[0212] Ability of individual metabolites and panels to distinguish PDAC from BPD (low-grade cysts) The validation set (set #2) was derived from the same study but analyzed separately. A separate cohort of subjects diagnosed with resectable PDAC and 102 PBD (trial Set #2). Individual classification performance ranged from 0.60 to 0.73 (Table 2). The fixed logistic regression model for the 5-metabolite panel yielded a mean mean of 0.70 (9 This resulted in an AUC of 0.573-0.833 (5% CI = 0.573-0.833), with a 15% sensitivity and 95% specificity. The degrees of oxidative stress were shown (Figure 10B and Table 19).

[0213] Combining metabolite and protein markers improves classification performance We have already developed a protein-derived biomarker panel for early-stage PDAC. was validated in the same independent cohort (Test Set #1) described herein. The Hyper Panel, which consists of a metabolite panel and a protein panel, is a protein panel. We investigated whether the classification performance improved compared to the training set (29 PDAC cases vs. 1 The AUC of the Hyperpanel in the 1000-case group (0 healthy controls) was 0.97 with a 95% CI. A metabolite panel for a 1% FPR value yielded an AUC of 0.9278–1.000. The sensitivity of only the hyperpanel in the training set is estimated to be 0.6897. When the variance is taken into account, this estimate improves statistically significantly to 0.8621 (corresponding (Two-sided p-value = 0.0390). C=0.95) and Hyperpanel (AUC=0.97) compared to 0.1074 A p-value of 0.05 is obtained (Figure 11A). Blinded validation on protein panel (Test Set #1) The corresponding estimate in yielded an AUC of 0.86, while Hyperpanel yielded an AUC of 0.0236 This resulted in an AUC of 0.92 with a corresponding one-sided p-value for the comparison equal to 0.05 (Figure 11B). This demonstrates an overall statistically significant improvement in the performance of the Hyperpanel compared to the protein panel. demonstrated significant improvements, demonstrating that metabolite and protein panels are complementary. show.

[0214] PDAC secretes acetylated polyamines To determine whether elevated plasma AcSperm and DAS are associated with disease states Five PDAC cell lines (CFPAC-1, MiaPaCa, SU8686, PANC Cell lysates and serum-free conditioned medium from the 03-27 and SW1990 strains were analyzed. Metabolomic analysis of cell lysates revealed that AcSperm Analysis of conditioned media revealed detectable levels of α- and β-actin. The positive rate of AcSperm accumulation was observed in 3 of the 5 cell lines. The mR of polyamine-related enzymes in the Badea dataset was observed (Fig. 12A). Examination of the expression of spermine synthase (SMS) in NA compared with adjacent control tissue and spermidine / spermine acetyltransferase (SAT1) significantly (vs. Paired T-test) showed PDAC-associated elevations, but spermidine synthase (SRM), poly Polyamine oxidase (PAOX) and spermine oxidase (SMOX) were significantly (Figure 11B)), which suggests that the acetylation of polyamines rather than their oxidation is responsible for the decrease. Together they suggest an increase and subsequent secretion.

[0215] PDAC catabolizes extracellular lysophosphatidylcholine To determine whether PDAC cells catabolize / remove extracellular lipids, PANC The lipid composition of serum-containing medium from Su8686 cells and Su8686 cells was analyzed at 24, 48 and 64°C. The analysis was performed using LPC(18:0) and LPC(20:3) ( The time-dependent decrease in several lysophospholipids (Fig. 13), including lysophospholipids (Fig. 12A), was observed. Furthermore, glycerophosphocholine, a degradation product of LPC, showed a time-dependent increase in the conditioned medium. (Fig. 12B), which collectively suggest active catabolism of extracellular LPC. Evaluation of mRNA expression of enzymes involved in the catabolism of lysophospholipids (Fig. 12C) was performed by Baden-Württemberg. a Soluble phospholipase A2-X compared to adjacent control tissue in the dataset. (PLA2G10), autotaxin (ENPP2) and lysophospholipase LYPL A significant (two-tailed Mann-Whitney U test) PDAC-associated increase in A1 was observed ( Figure 12D).

[0216] Consideration The primary objective of this study was to evaluate plasma metabolite-derived biomarker patterns in resectable PDAC. The objective of this study was to identify and validate the presence of nucleotides in the nucleus of the nucleus. Excisable from healthy individuals yielding an AUC of 0.89 in the test cohort (Test Set #1) Identifying and validating a five-marker metabolite biomarker panel capable of distinguishing between distinct PDAC cases The hyperpanel, consisting of a panel of metabolites and previously identified proteins, significantly improved classification performance compared to the protein panel alone (AUC: 0.92 vs. 0.86; p : 0.024; Test Set #1), which is consistent with the results of this metabolite panel. Emphasize complementary qualities.

[0217] Given the low prevalence of PDAC, multimarker signatures may be more effective than average-risk populations. Rather, it would be best suited to a screening program targeted at high-risk subjects. These include people over 50 years of age with new-onset diabetes mellitus, asymptomatic members of high-risk families, Subjects with chronic pancreatitis and patients with incidental diagnoses of mucin-secreting cysts of the pancreas were included. The metabolite-biomarker panel identified low-grade malignant tumors in two separate sample sets. Significantly distinguished PDAC from pancreatic cysts in validation set and test set #2, respectively. yielded AUCs equal to 0.69 and 0.70.

[0218] In particular, in contrast to previous findings, there was no significant difference in plasma concentrations between cases and their respective controls. No differences were observed in branched-chain amino acids (BCAAs). Consistent with the observation that there were no differences in plasma BCAAs among the samples, the predictive value of BCAAs was Most pronounced 2-5 years after diagnosis, with levels returning to baseline 0-2 years before diagnosis It is particularly noteworthy that

[0219] Alterations in polyamine metabolism have long been associated with tumorigenesis and hyperproliferative disorders, and Polyamine synthesis is closely related to the progression of apoptosis. Their catabolism is regulated by SAT1, whereas their catabolism is regulated by SAT2. findings suggest that putrescine and Ac in pancreatic cancer compared with histologically unaffected pancreas Conversely, the abundance of AcSpe was increased compared to healthy controls. Many polyamines, including rm, have already been found to be elevated in the serum of patients. findings in PDAC compared with adjacent control tissue in the Badea dataset. Elevated mRNA expression of SAT1 in the sera, and AcSperm and D in the cell lysates This is consistent with the detection of AS and their simultaneous accumulation in the conditioned medium (Fig. 11). The findings described in this and other studies are reflected in the plasma of subjects with PDAC. This indicates an amplification of polyamine catabolism, a concept that is currently being explored. In particular, elevated DAS is only attributed to pancreatic cancer. Rather, this is a more important consideration in its broader utility as a screening marker for cancer. It essentially suggests a general role.

[0220] Previous studies have shown that plasma LPC is significantly higher in healthy controls than in controls with HIV. Cell line data showed that the IL-16 expression level was significantly lower in PDAC compared with subjects with chronic pancreatitis. , a concept supported by gene expression data in the Badea dataset. Catabolizes certain lysophospholipids (Figure 12), thereby increasing the plasma LPC observed in PDAC subjects. Nevertheless, PDAC alone was associated with a significant reduction in the number of patients with PDAC. However, this cannot fully explain the decline in plasma LPC levels, especially in the early stages of the disease. Metastasis to the liver, an important organ that regulates liver function, has been shown to occur early in pancreatic cancer. Therefore, the decrease in plasma LPC may be due to the increased catabolism by cancer cells and the liver function that occurs concurrently with the disease. It is likely that this concept reflects both changes in the ability to This will require further investigation.

[0221] In conclusion, we developed a metabolite-derived biomarker panel for early-stage PDAC and have already The identified protein-based biomarker panel was validated to complement the existing one.

[0222] Other embodiments The above detailed description is provided to aid those skilled in the art in practicing the present disclosure. While the disclosure described and claimed herein is not intended to be limiting, the specific The scope should not be limited by this embodiment, as this embodiment is not intended to be limiting of the scope of the present disclosure. Any equivalent embodiments are intended to be illustrative of the present disclosure. Indeed, various modifications of the present disclosure are intended to be within the scope of the present disclosure. and the like, in addition to those shown and described herein, without departing from the spirit or scope of the present invention. Such modifications will be apparent to those skilled in the art from the above description. is intended to be included in the scope.

Claims

1. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; measuring the level of CA19-9 antigen in said biological sample; measuring the level of TIMP1 antigen in said biological sample; measuring the level of LRG1 antigen in said biological sample. Including, The amounts of CA19-9 antigen, TIMP1 antigen and LRG1 antigen indicate that the patient has pancreatic ductal adenocarcinoma. and classifying the patient as sensitive to pancreatic ductal adenocarcinoma or as not sensitive to pancreatic ductal adenocarcinoma.

2. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; contacting the sample with a first reporter molecule that binds to the CA19-9 antigen; 、 contacting the sample with a second reporter molecule that binds to the TIMP1 antigen; contacting the sample with a third reporter molecule that binds to the LRG1 antigen; Including, The first reporter molecule, the second reporter molecule and the third reporter molecule The amount of the gene may be used to identify the patient as susceptible to pancreatic ductal adenocarcinoma or as a candidate for pancreatic ductal adenocarcinoma. A method for classifying something as not being

3. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; having means for binding to CA19-9 antigen, TIMP1 antigen and LRG1 antigen Providing a surface, incubating the surface with the biological sample; contacting the surface with a first reporter molecule that binds to the CA19-9 antigen; contacting the surface with a second reporter molecule that binds to the TIMP1 antigen; contacting the surface with a third reporter molecule that binds to the LRG1 antigen; measuring the amount of said first reporter molecule associated with said surface; measuring the amount of said second reporter molecule associated with said surface; measuring the amount of said third reporter molecule associated with said surface. Including, The first reporter molecule, the second reporter molecule and the third reporter molecule The amount of the gene may be used to identify the patient as susceptible to pancreatic ductal adenocarcinoma or as a disease susceptible. A method for classifying something as not being

4. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; providing a first surface having means for binding to a CA19-9 antigen; providing a second surface having means for binding to a TIMP1 antigen; providing a third surface having means for binding to an LRG1 antigen; incubating the first surface with the biological sample; incubating the second surface with the biological sample; incubating the third surface with the biological sample; contacting the first surface with a first reporter molecule that binds to the CA19-9 antigen; and, contacting the second surface with a second reporter molecule that binds to the TIMP1 antigen; 、 contacting the third surface with a third reporter molecule that binds to the LRG1 antigen; determining the amount of said first reporter molecule associated with said first surface; determining the amount of said second reporter molecule associated with said second surface; measuring the amount of said third reporter molecule associated with said third surface. Including, The first reporter molecule, the second reporter molecule and the third reporter molecule The amount of the gene may be used to identify the patient as susceptible to pancreatic ductal adenocarcinoma or as a disease susceptible. A method for classifying something as not being

5. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; having means for binding to CA19-9 antigen, TIMP1 antigen and LRG1 antigen Providing a surface, incubating the surface with the biological sample; contacting the surface with a first relay molecule that binds to the CA19-9 antigen; contacting the surface with a second relay molecule that binds to the TIMP1 antigen; contacting the surface with a third relay molecule that binds to the LRG1 antigen; contacting the surface with a first reporter molecule that binds to the first relay molecule; 、 contacting the surface with a second reporter molecule that binds to the second relay molecule; 、 contacting the surface with a third reporter molecule that binds to the third relay molecule; 、 the first reporter molecule associated with the first relay molecule and the CA19-9 antigen; Measuring the quantity of the child, the second relay molecule and the second reporter molecule associated with the TIMP1 antigen. measuring the amount of The third relay molecule and the third reporter molecule associated with the LRG1 antigen. Measuring quantity Including, The first reporter molecule, the second reporter molecule and the third reporter molecule Said amount of a gene may be used to identify said patient as susceptible to pancreatic ductal adenocarcinoma or as a candidate for pancreatic ductal adenocarcinoma. A method of classifying something as not passive.

6. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; providing a first surface having means for binding to a CA19-9 antigen; providing a second surface having means for binding to a TIMP1 antigen; providing a third surface having means for binding to an LRG1 antigen; incubating the first surface with the biological sample; incubating the second surface with the biological sample; incubating the third surface with the biological sample; contacting the first surface with a first relay molecule that binds to the CA19-9 antigen; contacting the second surface with a second relay molecule that binds to a TIMP1 antigen; contacting the third surface with a third relay molecule that binds to an LRG1 antigen; contacting the first surface with a first reporter molecule that binds to the first relay molecule; To do so, contacting the second surface with a second reporter molecule that binds to the second relay molecule; To do so, contacting the third surface with a third reporter molecule that binds to the third relay molecule; To do so, the first reporter molecule associated with the first relay molecule and the CA19-9 antigen; Measuring the quantity of the child, the second relay molecule and the second reporter molecule associated with the TIMP1 antigen. measuring the amount of The third relay molecule and the third reporter molecule associated with the LRG1 antigen. Measuring quantity Including, The first reporter molecule, the second reporter molecule and the third reporter molecule Said amount of a gene may be used to identify said patient as susceptible to pancreatic ductal adenocarcinoma or as a candidate for pancreatic ductal adenocarcinoma. A method of classifying something as not passive.

7. At least one of the surfaces is selected from the group consisting of CA19-9, TIMP1 and LRG1.

3. The method of claim 2, further comprising: The method according to any one of claims 1 to 6.

8. 7. The method according to claim 3, wherein at least one of the surfaces is a surface of a solid particle. The method described.

9. The method of claim 8 , wherein the solid particles are beads.

10. 7. The method according to claim 2, wherein at least one of the reporter molecules is linked to an enzyme. The method according to any one of claims 1 to 5.

11. At least one of the first reporter molecules generates a detectable signal. Item 7. The method according to any one of Items 2 to 6.

12. 12. The method of claim 11, wherein the detectable signal is detectable by spectrometry. 。

13. The method of claim 12 , wherein the spectroscopic method is mass spectrometry.

14. The method of any one of claims 2 to 4, wherein the first reporter molecule selectively binds to the CA19-9 antigen.

10. The method according to any one of claims 1 to 9.

15. The method according to any one of claims 2 to 4, wherein the second reporter molecule selectively binds to a TIMP1 antigen. The method according to any one of claims 1 to 4.

16. The third reporter molecule selectively binds to the LRG1 antigen. The method according to any one of claims 1 to 5.

17. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; The biological sample is contacted with a CA19-9 antibody, and a bond between the antigen and the antibody is detected. and determining the level of CA19-9 antigen in the biological sample by observing whether or not the antigen is present. and, contacting the biological sample with a TIMP1 antibody and determining binding between the antigen and the antibody; measuring the level of TIMP1 antigen in said biological sample by observing contacting the biological sample with an LRG1 antibody and detecting binding between the antigen and the antibody; determining the level of LRG1 antigen in said biological sample by observing; As determined by measurements of CA19-9, TIMP1 and LRG1 levels, The patient's condition may be determined as being susceptible to pancreatic ductal adenocarcinoma or not susceptible to pancreatic ductal adenocarcinoma. Assigning either A method comprising:

18. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; measuring the level of CA19-9 antigen in said biological sample; measuring the level of TIMP1 antigen in said biological sample; measuring the level of LRG1 antigen in said biological sample; determining the level of CA19-9 antigen relative to a first standard value, the ratio being To predict and determine ductal adenocarcinoma determining the level of TIMP1 antigen relative to a second standard value, the ratio being indicative of pancreatic ductal To predict and determine adenocarcinoma determining the level of LRG1 antigen compared to a third standard value, the ratio being indicative of pancreatic ductal gland predicting, determining cancer, and determined by statistical analysis of the ratios of CA19-9, TIMP1 and LRG1 levels. The patient's condition may be characterized as being susceptible to pancreatic ductal adenocarcinoma or as being susceptible to pancreatic ductal adenocarcinoma, such that Assigning either a sensitivity or a A method comprising:

19. 1. A method for predicting a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; the levels of CA19-9 antigen, TIMP1 antigen, and LRG1 antigen in said biological sample measuring the As determined by statistical analysis of the CA19-9, TIMP1 and LRG1 levels To calculate the predictor, A method comprising:

20. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; the levels of CA19-9 antigen, TIMP1 antigen, and LRG1 antigen in said biological sample measuring the The levels of the CA19-9 antigen, the TIMP1 antigen, and the LRG1 antigen in the biological sample The patient's status is determined to be susceptible to pancreatic ductal adenocarcinoma as determined by statistical analysis of the results of a randomized controlled trial. to be either susceptible or not susceptible to pancreatic ductal adenocarcinoma A method comprising:

21. A method for detecting susceptibility to pancreatic ductal adenocarcinoma using a biological sample obtained from a patient There was, Regarding the level of CA19-9 antigen present in the biological sample, assaying using at least one antibody or antibody fraction specific for the antigen; and Beauty Regarding the level of TIMP1 antigen present in the biological sample, Assaying using at least one antibody or antibody fraction specific for Regarding the level of LRG1 antigen present in the biological sample, assaying using at least one antibody or antibody fraction different from the antibody; The levels of the CA19-9 antigen, the TIMP1 antigen, and the LRG1 antigen are indicative of pancreatic ductal adenocarcinoma. determining whether the patient is indicative of having A method comprising:

22. 1. A method for detecting susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from a subject; Immunoassay of the sample with an anti-CA19-9 antibody or its antigen-binding fragment. To carry out, performing an immunoassay on the sample using an anti-LRG1 antibody or an antigen-binding fragment thereof; To do, An immunoassay is performed on the sample using an anti-TIMP1 antibody or an antigen-binding fragment thereof. To give Including, Binding of the antibody is indicative of pancreatic ductal adenocarcinoma in the subject, and the immunoassay , a method capable of detecting early stage pancreatic ductal adenocarcinoma.

23. 1. A method for detecting susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from an individual; conducting an immunoassay with an anti-CA19-9 antibody or antigen-binding fragment thereof; conducting an immunoassay with an anti-LRG1 antibody or antigen-binding fragment thereof; conducting an immunoassay with an anti-TIMP1 antibody or antigen-binding fragment thereof; CA19-9 antigen, TIMP1 antigen, and LRG1 antigen levels are associated with pancreatic ductal adenocarcinoma To determine whether the patient is an indicator A method comprising:

24. The determination of CA19-9, LRG1 and TIMP1 levels is performed substantially simultaneously.

24. The method according to any one of claims 1 to 23.

25. The determination of CA19-9, LRG1 and TIMP1 levels is performed stepwise.

24. The method of any one of 1 to 23.

26. Patient history information to assign patients with or without pancreatic ductal adenocarcinoma 24. The method of any one of claims 1 to 23, comprising containing

27. Patients assigned to have pancreatic ductal adenocarcinoma will be administered at least one alternative diagnostic test. The method of any one of claims 1 to 23, comprising:

28. The at least one alternative diagnostic test is at least one assay of ctDNA or 28. The method of claim 27, comprising sequencing.

29. A kit for the method according to any one of claims 1 to 23, comprising: a first solute for the detection of CA19-9 antigen; a second solute for the detection of the LRG1 antigen, and Third Solute for Detection of TIMP1 Antigen A kit comprising a reagent solution comprising:

30. A kit for the method according to any one of claims 1 to 23, comprising: a first reagent solution containing a first solute for detecting the CA19-9 antigen; a second reagent solution containing a second solute for the detection of the LRG1 antigen; and Third reagent solution containing a third solute for detecting TIMP1 antigen Kit including:

31. 29 or 3, comprising a device for contacting the reagent solution with a biological sample.

10. The kit according to claim 0.

32. at least one surface having means for binding at least one antigen. The kit according to claim 29 or 30.

33. The at least one antigen is selected from the group consisting of CA19-9, LRG1 and TIMP1.

33. The kit of claim 32, wherein the kit is selected from the group consisting of:

34. 34. The method of claim 33, wherein the at least one surface comprises means for binding to ctDNA. The kit described.

35. The level of (N1 / N8)-acetylspermidine (AcSperm) in the biological sample Measuring the bell, measuring the level of diacetylspermine (DAS) in the biological sample; The level of lysophosphatidylcholine (LPC) (18:0) in the biological sample is measured. To determine, The level of lysophosphatidylcholine (LPC) (20:3) in the biological sample was measured. and measuring the level of an indole derivative in said biological sample. further comprising (N1 / N8)-acetylspermidine (AcSperm), diacetylspermine ( DAS), lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine The amount of LPC (20:3) and the indole derivative is used to treat the patient with pancreatic ductal adenocarcinoma.

10. The method of claim 1, wherein the subject is classified as being susceptible to pancreatic ductal adenocarcinoma or not susceptible to pancreatic ductal adenocarcinoma.

10. The method according to any one of the preceding claims.

36. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; The level of (N1 / N8)-acetylspermidine (AcSperm) in the biological sample Measuring the bell, measuring the level of diacetylspermine (DAS) in the biological sample; The level of lysophosphatidylcholine (LPC) (18:0) in the biological sample is measured. To determine, The level of lysophosphatidylcholine (LPC) (20:3) in the biological sample was measured. and measuring the level of an indole derivative in said biological sample. Including, (N1 / N8)-acetylspermidine (AcSperm), diacetylspermine ( DAS), lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine The amount of LPC (20:3) and the indole derivative is used to treat the patient with pancreatic ductal adenocarcinoma. and classifying the patient as susceptible to pancreatic ductal adenocarcinoma or as not susceptible to pancreatic ductal adenocarcinoma.

37. A panel of plasma-derived biomarkers and a protein marker panel for pancreatic ductal adenocarcinoma 1. A method for determining a patient's susceptibility to a steroid drug, comprising: The plasma-derived biomarker panel includes (N1 / N8)-acetylspermidine (Ac Sperm), diacetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine (LPC) (20:3) and indole derivatives Including the body, The protein biomarker panel includes CA19-9, LRG1, and TIMP1. Including, The method comprises: obtaining a biological sample from said patient; the plasma-derived biomarkers and the protein biomarkers in the biological sample Measuring the level of Including, The amounts of the plasma-derived biomarkers and the protein biomarkers are used to identify the patient. Classify as sensitive to pancreatic ductal adenocarcinoma or not sensitive to pancreatic ductal adenocarcinoma, method.

38. One or more protein biomarkers and one or more metabolite markers 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising determining the level of 、 obtaining a biological sample from said patient; contacting the sample with a first reporter molecule that binds to the CA19-9 antigen; 、 contacting the sample with a second reporter molecule that binds to the TIMP1 antigen; contacting the sample with a third reporter molecule that binds to the LRG1 antigen; and Call determining the level of said one or more biomarkers, Alternatively, the plurality of biomarkers may include (N1 / N8)-acetylspermidine (AcSper m), diacetylspermine (DAS), lysophosphatidylcholine (LPC) (18: 0), lysophosphatidylcholine (LPC) (20:3) and indole derivatives Selected from the group Including, The first reporter molecule, the second reporter molecule, the third reporter molecule and and the amount of said one or more biomarkers determines whether said patient is susceptible to pancreatic ductal adenocarcinoma. a method for classifying a patient as susceptible to or not susceptible to pancreatic ductal adenocarcinoma.

39. 1. A method for determining a patient's susceptibility to pancreatic ductal adenocarcinoma, comprising: obtaining a biological sample from said patient; the levels of CA19-9 antigen, TIMP1 antigen, and LRG1 antigen in said biological sample measuring the (N1 / N8)-acetylspermidine (AcSperm) in the biological sample, Diacetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0), From the group consisting of lysophosphatidylcholine (LPC) (20:3) and indole derivatives measuring the level of one or more metabolic markers selected from the group consisting of: CA19-9 antigen, TIMP1 antigen, LRG1 antigen, (N1 / N 8)-Acetylspermidine (AcSperm), diacetylspermine (DAS), Lysophosphatidylcholine (LPC) (18:0), Lysophosphatidylcholine (LPC) (20:3) and the indole derivatives are determined by statistical analysis of the levels Thus, the patient's condition may be defined as being susceptible to pancreatic ductal adenocarcinoma or as being susceptible to pancreatic ductal adenocarcinoma. Assigning either non-receptive or non-receptive A method comprising:

40. 1. A method of treating a patient suspected of being susceptible to pancreatic ductal adenocarcinoma, comprising: The patient is treated with a method according to any one of claims 36 to 39 for pancreatic ductal adenocarcinoma. Analyzing for susceptibility, administering a therapeutically effective amount of a treatment to said adenocarcinoma. A method comprising:

41. The treatment is surgery, chemotherapy, radiation therapy, targeted therapy, or a combination thereof.

41. The method of treatment according to claim 40.

42. A compound that selectively binds to an antigen selected from the group consisting of CA19-9, TIMP1, and LRG1.

41. The method according to claim 36, comprising at least one receptor molecule binding to the How to do it.

43. CA19-9, TIMP1, LRG, (N1 / N8)-acetylspermidine (AcS perm), diacetylspermine (DAS), lysophosphatidylcholine (LPC) ( 18:0), lysophosphatidylcholine (LPC) (20:3) or the indole derivative 41. The method according to claim 36, wherein detecting the amount of conductor comprises using solid particles. How to do it.

44. 44. The method of claim 43, wherein the solid particles are beads.

45. 41. The method of claim 36, wherein at least one of the reporter molecules is linked to an enzyme.

10. The method according to any one of claims 1 to 9.

46. At least one of the protein markers or metabolite markers has a detectable signal. The method according to any one of claims 36 to 40, wherein a null is generated.

47. Any of claims 36 to 40, wherein the detectable signal is detectable by spectrometry. The method according to any one of claims 1 to 5.

48. 48. The method of claim 47, wherein the spectroscopic method is mass spectrometry.

49. Patient history information to assign patients with or without pancreatic ductal adenocarcinoma 41. The method of any one of claims 36 to 40, comprising inclusion.

50. Patients assigned to have pancreatic ductal adenocarcinoma will be administered at least one alternative diagnostic test. The method of any one of claims 36 to 40, comprising:

51. The at least one alternative diagnostic test is at least one assay of ctDNA or 51. The method of claim 50, comprising sequencing.

52. A kit for the method according to any one of claims 36 to 40, comprising: a first solute for the detection of CA19-9 antigen; a second solute for the detection of LRG1 antigen; a third solute for the detection of TIMP1 antigen; (N1 / N8)-A quaternary solute for the detection of acetylspermidine (AcSperm) 、 a fifth solute for the detection of diacetylspermine (DAS); a sixth solute for the detection of lysophosphatidylcholine (LPC) (18:0); a seventh solute for the detection of lysophosphatidylcholine (LPC) (20:3), and An eighth solute for detecting the indole derivative A kit comprising a reagent solution comprising:

53. A kit for the method according to any one of claims 36 to 40, comprising: a first reagent solution containing a first solute for detecting the CA19-9 antigen; a second reagent solution containing a second solute for the detection of the LRG1 antigen; a third reagent solution containing a third solute for the detection of TIMP1 antigen; (N1 / N8)-A quaternary solute for the detection of acetylspermidine (AcSperm) a fourth reagent solution comprising: a fifth reagent solution containing a fifth solute for the detection of diacetylspermine (DAS); A sixth solute containing a sixth solute for the detection of lysophosphatidylcholine (LPC) (18:0) 6 reagent solution, A seventh solute containing a seventh solute for the detection of lysophosphatidylcholine (LPC) (20:3) 7 reagent solution, and an eighth reagent solution containing an eighth solute for detecting the indole derivative; Kit including:

54. 52 or 5, comprising a device for contacting the reagent solution with a biological sample.

3. The kit according to claim 3.

55. at least one surface having means for binding at least one antigen.

54. The kit of claim 52 or 53.

56. The at least one antigen is selected from the group consisting of CA19-9, LRG1 and TIMP1.

56. The kit of claim 55, wherein the kit is selected from the group consisting of:

57. 56. The method of claim 55, wherein said at least one surface comprises means for binding to ctDNA. The kit described.

58. A method of treating or preventing the progression of pancreatic ductal adenocarcinoma (PDAC) in a patient, comprising administering to said patient The levels of CA19-9 antigen, TIMP1 antigen and LRG1 antigen in the patient are and classifying the subject as having or susceptible to PDAC, the method comprising: i. administering a chemotherapeutic agent to said patient with PDAC; ii. administering therapeutic radiation to said patient with PDAC; and iii. For partial or complete surgical resection of cancerous tissue in said patient with PDAC. Surgery A method comprising one or more of:

59. The levels of the CA19-9 antigen, the TIMP1 antigen, and the LRG1 antigen are elevated.

59. The method of claim 58.

60. The levels of the CA19-9 antigen, TIMP1 antigen, and LRG1 antigen are measured in patients with PDAC. CA19-9 antigen, TIMP1 antigen and LRG1 antigen in reference patients or reference groups 59. The method of claim 58, wherein the level is elevated compared to the level of

61. 61. The method of claim 60, wherein the reference patient or group is healthy.

62. 59. The method of claim 58, wherein the AUC (95% CI) is at least 0.

850.

63. 59. The method of claim 58, wherein the AUC (95% CI) is at least 0.

900.

64. Classification of the patient as having PDAC was 0.01 with a specificity of 95% and 99%, respectively.

59. The method of claim 58, having a sensitivity of 0.849 and 0.

658.

65. The levels of the CA19-9 antigen, TIMP1 antigen and LRG1 antigen are measured in patients with chronic pancreatitis. CA19-9 antigen, TIMP1 antigen and LRG1 antigen in reference patients or reference groups 59. The method of claim 58, wherein the level is elevated compared to the level.

66. The levels of the CA19-9 antigen, TIMP1 antigen and LRG1 antigen are indicative of benign pancreatic disease. CA19-9 antigen, TIMP1 antigen and LRG1 antigen in reference patients or reference groups with 59. The method of claim 58, wherein the level is increased compared to the original level.

67. 67. The method of claim 66, wherein the AUC (95% CI) is at least 0.

850.

68. 67. The method of claim 66, wherein the AUC (95% CI) is at least 0.

900.

69. Classification of the patient as having PDAC was 0.01 with a specificity of 95% and 99%, respectively.

59. The method of claim 58, having a sensitivity of 0.849 and 0.

658.

70. 5. The PDAC is diagnosed at or before a borderline resectable stage.

8. The method according to claim 8.

71. 59. The method of claim 58, wherein the PDAC is diagnosed at a resectable stage.

72. A method of treating or preventing the progression of pancreatic ductal adenocarcinoma (PDAC) in a patient, comprising administering to said patient CA19-9 antigen, TIMP1 antigen, LRG1, N1 / N8)-acetylsperm AcSperm, diacetylspermine (DAS), lysophosphatidylcholine (LPC) (18:0), lysophosphatidylcholine (LPC) (20:3) and in The level of the dhole derivative identifies the patient as having or susceptible to PDAC. and classifying the method as being sensitive to the i) administering a chemotherapeutic agent to said patient with PDAC; ii) administering therapeutic radiation to said patient with PDAC; and iii) for partial or complete surgical resection of cancerous tissue in said patient with PDAC. Surgery A method comprising one or more of:

73. The levels of the CA19-9 antigen, the TIMP1 antigen, and the LRG1 antigen are elevated.

73. The method of claim 72.

74. The levels of the CA19-9 antigen, TIMP1 antigen, and LRG1 antigen are measured in patients with PDAC. CA19-9 antigen, TIMP1 antigen and LRG1 antigen in reference patients or reference groups 73. The method of claim 72, wherein the level is elevated compared to the level of

75. 73. The method of claim 72, wherein the reference patient or group is healthy.

76. The levels of the CA19-9 antigen, TIMP1 antigen and LRG1 antigen are measured in patients with chronic pancreatitis. CA19-9 antigen, TIMP1 antigen and LRG1 antigen in reference patients or reference groups 73. The method of claim 72, wherein the level is elevated compared to the level.

77. The levels of the CA19-9 antigen, TIMP1 antigen and LRG1 antigen are indicative of benign pancreatic disease. CA19-9 antigen, TIMP1 antigen and LRG1 antigen in reference patients or reference groups with 73. The method of claim 72, wherein the level is increased compared to the original level.

78. 73. The method of claim 72, wherein the patient is at high risk for PDAC.

79. The patient is over 50 years old with new onset diabetes mellitus, has chronic pancreatitis, or has pancreatic cancer. Incidentally diagnosed with a mucin-secreting cyst of the liver or asymptomatic in one of the high-risk groups 79. The method of any one of claims 58 to 78, which is a pedigree.

80. 1. A method of treating a patient suspected of being susceptible to pancreatic ductal adenocarcinoma, comprising: The patient is diagnosed with a susceptibility to pancreatic ductal adenocarcinoma by the method of any one of claims 1 to 79. Analyzing in terms of receptivity, administering a therapeutically effective amount of a treatment to said adenocarcinoma. A method comprising:

81. The treatment is surgery, chemotherapy, radiation therapy, targeted therapy, or a combination thereof.

81. The method of treatment according to claim 80.

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