JP2024117521A - Method of detecting pancreatic cancer - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide novel marker peptides for diagnosis of pancreatic cancer.

SOLUTION: A method of detecting pancreatic cancer in a subject is provided, the method comprising measuring at least either of a peptide having an amino acid sequence represented by the sequence number 1 and a peptide having an amino acid sequence represented by the sequence number 2 in a biological sample of a subject.

SELECTED DRAWING: Figure 1

COPYRIGHT: (C)2024,JPO&INPIT

Description

The present invention relates to a method for detecting pancreatic cancer using a peptide marker, and more specifically to a method for determining pancreatic cancer using a peptide marker, determining pancreatic cancer patients (responders) who will respond to a therapeutic drug (companion diagnostic method), determining the effectiveness of prevention, determining the effectiveness of treatment, a testing method for early diagnosis, a testing method for early treatment, and a method for screening substances.

Pancreatic cancer is known as an intractable cancer with the lowest survival rate of all cancers, as symptoms are subtle and early detection is difficult. For this reason, there is a strong demand for a method to diagnose pancreatic cancer early.

Patent Document 1 discloses that α-1-antitrypsin (sequence number 418) and fibrinogen α-chain (sequence number 758) are proteins that can serve as diagnostic markers for pancreatic cancer.

Patent document 2 discloses that peptides derived from the A chain of blood coagulation factor XIII can be used as diagnostic markers for cancers, including pancreatic cancer.

Patent document 3 discloses that a peptide consisting of amino acids 383-413 of α-1-antitrypsin can be used as a diagnostic marker for pancreatic cancer.

Non-patent literature 1 reports that the expression of α-2-HS-glycoprotein differs between pancreatic cancer patients and healthy individuals. However, it does not describe the sequence of a specific peptide that could be a diagnostic marker.

Non-Patent Document 2 discloses, as marker peptides for pancreatic cancer, a peptide consisting of the 14-38 amino acid sequence of blood coagulation factor XIII, a peptide consisting of the 45-71 amino acids of the fibrinogen β chain, a peptide consisting of the 328-363 amino acids of prothrombin, a peptide consisting of the 529-574 amino acids of the fibrinogen α chain, and a peptide consisting of the 576-629 amino acids of the fibrinogen α chain.

Special table 2016-519285 WO2018007555A1 WO2020223646A1

Molecular Medicine REREPORORTS 3 651-656 translational proteomics 2 (2014) 39-51

The object of the present invention is to detect pancreatic cancer quickly and with high accuracy by measuring biomarker peptides in a subject's biological sample.

According to the present invention, the following aspects are provided.
Item 1.
A method for detecting pancreatic cancer in a subject, the method comprising measuring at least one of a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2 in a biological sample from the subject.
Item 2.
Item 2. The method according to Item 1, further comprising measuring one or more peptides selected from the group consisting of a peptide consisting of the amino acid sequence represented by SEQ ID NO: 3, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 4, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 6, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 8, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 9, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 10.
Item 3.
The method according to claim 1, which is a method for detecting pancreatic cancer in a subject, comprising measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2 in a biological sample from the subject.
Item 4.
Item 2. The method according to item 1, which is a method for detecting pancreatic cancer in a subject, comprises measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 6, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 8 in a biological sample from the subject.
Item 5.
Item 2. The method according to item 1, which is a method for detecting pancreatic cancer in a subject, comprising measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 3, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7 in a biological sample from the subject.
Item 6.
Item 2. The method according to item 1, which is a method for detecting pancreatic cancer in a subject, comprises measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 3, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 9 in a biological sample from the subject.
Item 7.
Item 2. The method according to item 1, which is a method for detecting pancreatic cancer in a subject, comprising measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 10 in a biological sample from the subject.
Item 8.
8. The method according to any one of claims 1 to 7, comprising subjecting the biological sample to mass spectrometry.
Item 9.
Item 8. The method according to any one of Items 1 to 7, wherein the biological sample is a body fluid selected from the group consisting of blood, plasma, serum, saliva, urine, cerebrospinal fluid, bone marrow fluid, pleural fluid, peritoneal fluid, synovial fluid, sweat, tears, aqueous humor, vitreous fluid, and lymphatic fluid.
Item 10.
The method according to any one of Items 1 to 7, wherein the detection of pancreatic cancer is a test for determining pancreatic cancer, a test for determining a preventive effect for pancreatic cancer, a test for determining a therapeutic effect for pancreatic cancer, a test for determining a pancreatic cancer patient for whom a therapeutic drug is effective, a test for determining a therapeutic drug effective for an individual pancreatic cancer patient, a test for diagnosing pancreatic cancer, or a test for treating pancreatic cancer.
Item 11.
A pancreatic cancer detection kit comprising an internal standard in which at least one of a peptide having an amino acid sequence represented by sequence number 1 and a peptide having an amino acid sequence represented by sequence number 2 is labeled with a stable isotope.
Item 12.
A kit for detecting pancreatic cancer, comprising an antibody against at least one of a peptide having an amino acid sequence represented by SEQ ID NO:1 and a peptide having an amino acid sequence represented by SEQ ID NO:2.
Item 13.
A detection agent for pancreatic cancer comprising an antibody against at least one of a peptide having an amino acid sequence represented by SEQ ID NO: 1 and a peptide having an amino acid sequence represented by SEQ ID NO: 2 as a detection reagent.
Item 14.
A computer-implemented method for determining the possibility of pancreatic cancer in a subject, the method comprising the steps of: acquiring quantitative data on at least one or more peptides selected from the peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 of the present invention and/or the peptide consisting of the amino acid sequence represented by SEQ ID NO: 2, and the peptides consisting of the amino acid sequences represented by SEQ ID NOs: 3 to 10, in a biological sample from the subject; and applying the acquired data to a multivariate logistic regression model that is a function of the two, three, or four or more types of peptides, to determine a predicted value for the possibility of pancreatic cancer in the subject.
Item 15.
A biomarker for detecting pancreatic cancer, comprising at least one of a peptide consisting of the amino acid sequence represented by SEQ ID NO:1 and a peptide consisting of the amino acid sequence represented by SEQ ID NO:2.

The present invention allows pancreatic cancer to be determined quickly and with extremely high reliability, making it possible to diagnose and/or treat pancreatic cancer early.

Scatter plot of risk index of subjects in each group evaluated by multivariate logistic regression using six types of multimarkers. NC: healthy subjects, PAC: pancreatic cancer patients.

The present invention provides novel and useful marker peptides for detecting pancreatic cancer (hereinafter, sometimes collectively referred to as "peptides of the present invention").

In this specification, "detection" of pancreatic cancer includes the determination of pancreatic cancer, the determination of pancreatic cancer patients (responders) for whom a therapeutic drug is effective (companion diagnostic method), the determination of the preventive effect of pancreatic cancer, the determination of the therapeutic effect of pancreatic cancer, a test method for diagnosing (particularly early diagnosis) pancreatic cancer, and a test method for treating (particularly early treatment) pancreatic cancer. "Determination" of pancreatic cancer includes not only determining the presence or absence of pancreatic cancer, but also preventatively determining the possibility of developing pancreatic cancer, predicting the prognosis of pancreatic cancer after treatment, and determining the therapeutic effect of a therapeutic drug for pancreatic cancer. Screening methods for substances include screening methods for substances useful for "detection," "determination," and "treatment" of pancreatic cancer.

In this specification, "affected" includes "onset."

As used herein, "treatment" means curing or ameliorating a disease or symptom, or suppressing a symptom, and includes "prevention." "Prevention" means preventing the onset of a disease or symptom.

As used herein, "peptide" refers to a molecule formed by linking 2 to 100 amino acids.

The present invention includes a method for detecting or determining pancreatic cancer in a subject, which comprises measuring at least one of a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2 in a biological sample from the subject.

QGVNDNEEGFFSAR (SEQ ID NO:1)
TVVQPSVGAAAGPVVPPCPGRIRHFKV (SEQ ID NO: 2)
The peptide consisting of the amino acid sequence represented by SEQ ID NO:1 is a partial sequence of the fibrinogen β chain, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of approximately 1552.67.

The peptide consisting of the amino acid sequence represented by SEQ ID NO:2 is a partial sequence of α-2-HS-glycoprotein, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of about 2739.52.

The actual measured mass values may vary slightly depending on the measurement method and equipment used. Therefore, when using a mass spectrometer, for example, the term "approximately" refers to an error of within ±0.5%, preferably within ±0.3%, and more preferably within ±0.1%.

The expression levels of each peptide consisting of the amino acid sequence represented by SEQ ID NO:1 and SEQ ID NO:2 correlate with the presence or absence of pancreatic cancer. Specifically, the concentration or amount in serum of the peptide consisting of the amino acid sequence represented by SEQ ID NO:1 is about 5 times higher in pancreatic cancer patients than in healthy individuals, and the concentration or amount in serum of the peptide consisting of the amino acid sequence represented by SEQ ID NO:2 is lower in pancreatic cancer patients than in healthy individuals.

The testing, determination, and diagnostic methods for detecting the peptides of the present invention as biomarkers can be used in place of or in combination with conventional methods for diagnosing pancreatic cancer to detect or diagnose pancreatic cancer, or to assist in the detection or diagnosis of pancreatic cancer.

In this specification, when we say "a peptide consisting of the amino acid sequence represented by SEQ ID NO:1", unless otherwise specified, such a peptide includes not only a peptide consisting of the amino acid sequence represented by SEQ ID NO:1 in which each amino acid is unmodified, but also a peptide consisting of the amino acid sequence represented by SEQ ID NO:1 in which one or more amino acids have been modified while maintaining the type of amino acid. Such modifications include oxidation by binding of an oxygen atom, phosphorylation, N-acetylation, S-cysteinylation, etc. Similarly, unless otherwise specified, each of the peptides consisting of the amino acid sequences represented by SEQ ID NO:2 to 10 includes peptides in which each amino acid is unmodified or modified. This definition also applies to each of the peptides consisting of the amino acid sequences represented by SEQ ID NO:2 to 10.

The peptide consisting of the amino acid sequence represented by SEQ ID NO:1 is preferably pyroglutamylated at the first Gln at the N-terminus.

In one embodiment, the method of the present invention for detecting pancreatic cancer in a subject includes measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO:1 in a biological sample from the subject.

In one embodiment, the method for detecting pancreatic cancer in a subject of the present invention includes measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO:2 in a biological sample from the subject.

In one embodiment, a method for detecting pancreatic cancer in a subject includes measuring both a peptide consisting of the amino acid sequence represented by SEQ ID NO:1 and a peptide consisting of the amino acid sequence represented by SEQ ID NO:2 in a biological sample from the subject.

The method of the present invention for detecting pancreatic cancer in a subject may include measuring an additional peptide in addition to the peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and/or the peptide consisting of the amino acid sequence represented by SEQ ID NO: 2 in the subject's biological sample. Such additional peptides include peptides consisting of the amino acid sequences represented by SEQ ID NOs: 3 to 10 below.

GHRPLDKKREEAPSLRPAPPPISGGGY (SEQ ID NO: 3)
SETSRTAFGGRRAVPPNNSNAAEDDLPTVELQGVVPR (SEQ ID NO: 4)
SIPPEVKFNKPFVFLMIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:5)
TFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGR (SEQ ID NO: 6)
TFPGFFSPMLGEFVSETESRGSESGIFTNTKESSSHHPGIAEFPSRG (SEQ ID NO: 7)
SSSYSKQFTSSTSYNRGDSTFESKSYKMADEAGSEADHEGTHSTKRGHAKSRPV (SEQ ID NO: 8)
SVPNGPSPEEVEQQKRQQPGPSEHIERRVSNAG (SEQ ID NO: 9)
VKVLDAVRGSPAIN (SEQ ID NO:10)

The peptide consisting of the amino acid sequence represented by SEQ ID NO:3 is a partial sequence of the fibrinogen β chain, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of about 2882.54.

The peptide consisting of the amino acid sequence represented by SEQ ID NO:4 is a partial sequence of the A chain of blood coagulation factor XIII, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of about 3949.98.

A peptide consisting of the amino acid sequence represented by SEQ ID NO:5 is a partial sequence of α-1-antitrypsin, and when the 16th methionine (the 398th methionine from the N-terminus of the α-1-antitrypsin protein) is oxidized, the calculated monoisotopic mass by mass spectrometry [M+H] ⁺ is approximately 4149.23, and when the 16th and 27th methionines (the 409th methionine from the N-terminus of the α-1-antitrypsin protein) are oxidized, the calculated monoisotopic mass by mass spectrometry [M+H] ⁺ is approximately 4165.22.

The peptide consisting of the amino acid sequence represented by SEQ ID NO:6 is a partial sequence of prothrombin, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of about 4208.03.

The peptide consisting of the amino acid sequence shown in SEQ ID NO: 7 is a partial sequence of the fibrinogen α-chain, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of approximately 5078.35.

The peptide consisting of the amino acid sequence shown in SEQ ID NO:8 is a partial sequence of the fibrinogen α-chain, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of approximately 5917.70.

The peptide consisting of the amino acid sequence represented by SEQ ID NO:9 is a partial sequence of the hemodilator-stimulated phosphoprotein, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of approximately 3623.79.

The peptide consisting of the amino acid sequence represented by SEQ ID NO:10 is a partial sequence of transthyretin, and has a monoisotopic mass calculated by mass spectrometry, [M+H] ⁺ , of approximately 1438.83.

Examples of specific amino acid modifications of the peptide of the present invention include acetylation of the first serine of the peptide consisting of the amino acid sequence represented by SEQ ID NO:4, oxidation of the methionine at the 16th position (398th position from the N-terminus of the α-1-antitrypsin protein) of the peptide consisting of the amino acid sequence represented by SEQ ID NO:5, oxidation of the methionine at the 27th position (409th position from the N-terminus of the α-1-antitrypsin protein) of the peptide consisting of the amino acid sequence represented by SEQ ID NO:5, S-cysteinylation of the 9th cysteine (336th position from the N-terminus of the prothrombin protein) of the peptide consisting of the amino acid sequence represented by SEQ ID NO:6, oxidation of the 9th methionine (536th position of the fibrinogen α chain protein) of the peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and oxidation of the 28th methionine (603rd position of the fibrinogen α chain protein) of the peptide consisting of the amino acid sequence represented by SEQ ID NO:8. Even in such cases, as long as the peptide has any of the amino acid sequences represented by SEQ ID NOs:1 to 10, it is included in the scope of the present invention. The above-mentioned modified peptides consisting of the amino acid sequences represented by SEQ ID NOs: 1 to 10 can be distinguished from unmodified peptides by mass spectrometry, and the scope of the present invention includes the use of either modified or unmodified peptides in appropriate situations for the detection, diagnosis, and treatment of diseases.

The expression level of each peptide consisting of the amino acid sequences represented by SEQ ID NOs: 3 to 10 correlates with the presence or absence of pancreatic cancer. Specifically, the serum concentration or amount of peptides consisting of the amino acid sequences represented by SEQ ID NOs: 3, 5, 6, 8, and 9 is higher in pancreatic cancer patients than in healthy individuals, and the serum concentration or amount of peptides consisting of the amino acid sequences represented by SEQ ID NOs: 4, 7, and 10 is lower in pancreatic cancer patients than in healthy individuals.

By using one, two, three, four, five, six, seven, or eight of the peptides consisting of the amino acid sequences represented by SEQ ID NO:3 to 10 in combination with a peptide consisting of the amino acid sequence represented by SEQ ID NO:1 and/or a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, the disease can be detected with higher accuracy.

In one embodiment, the peptide of the present invention measured in the method for detecting or determining pancreatic cancer in a subject includes a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and at least one peptide selected from the peptides consisting of the amino acid sequences represented by SEQ ID NOs: 2 to 10. By combining two or more peptides, the detection accuracy is increased.

In one embodiment, the peptide of the present invention measured in the method for detecting or determining pancreatic cancer in a subject includes a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2 and at least one peptide selected from peptides consisting of the amino acid sequences represented by SEQ ID NOs: 1, 3 to 10. By combining two or more peptides, the detection accuracy is increased.

In one embodiment, the peptides of the present invention measured in the method for detecting or determining pancreatic cancer in a subject include a peptide having an amino acid sequence represented by SEQ ID NO: 1, a peptide having an amino acid sequence represented by SEQ ID NO: 2, and at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs: 3 to 10. By combining two or more peptides, the detection accuracy is increased.

In a preferred embodiment, the peptides of the present invention measured in the method for detecting or determining pancreatic cancer in a subject include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:6, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:8 in the subject's biological sample. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides of the present invention measured in the method for detecting or determining pancreatic cancer in a subject include a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 3, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7 in the subject's biological sample. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides of the present invention measured in a method for detecting or determining pancreatic cancer in a subject include a peptide having an amino acid sequence represented by SEQ ID NO: 1 and/or a peptide having an amino acid sequence represented by SEQ ID NO: 2 in a biological sample from the subject, at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs: 3 to 8, and a peptide having an amino acid sequence represented by SEQ ID NO: 9. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides of the present invention measured in the method for detecting or determining pancreatic cancer in a subject include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:9 in the subject's biological sample. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides of the present invention measured in the method for detecting or determining pancreatic cancer in a subject include a peptide having an amino acid sequence represented by SEQ ID NO: 1 and/or a peptide having an amino acid sequence represented by SEQ ID NO: 2 in the subject's biological sample, at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs: 3 to 8, and a peptide having an amino acid sequence represented by SEQ ID NO: 10. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides of the present invention measured in a method for detecting or determining pancreatic cancer in a subject include a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 10 in a biological sample from the subject. Such a combination of peptides significantly increases the detection accuracy.

The subject includes a patient suspected of having pancreatic cancer, and a "patient suspected of having pancreatic cancer" may be a person who the subject himself/herself subjectively suspects (not limited to those who have some subjective symptoms, but including those who simply wish to undergo a preventive checkup), or a person who has been determined or diagnosed with pancreatic cancer based on some objective grounds (for example, a person who has undergone an abdominal imaging test triggered by abdominal pain, back pain, worsening diabetes, etc. and has been diagnosed with pancreatic cancer). "Measuring a peptide" refers to measuring the concentration, amount, or signal intensity of a peptide.

The subject-derived biological sample to be used as the test sample is not particularly limited, but is preferably one that is minimally invasive to the subject. Examples of such biological samples include blood, plasma, serum, saliva, urine, tears, sweat, and other fluids that can be easily collected from a living body, and cerebrospinal fluid, bone marrow fluid, pleural effusion, ascites, synovial fluid, aqueous humor, vitreous humor, and lymphatic fluid that are relatively easy to collect. In one embodiment, the biological sample is a body fluid selected from the group consisting of blood, plasma, serum, saliva, urine, cerebrospinal fluid, bone marrow fluid, pleural effusion, ascites, synovial fluid, tears, aqueous humor, vitreous humor, and lymphatic fluid. When serum or plasma is used, blood is collected from the subject according to a conventional method, and the test sample to be analyzed can be prepared by directly collecting blood without pretreatment or by separating the liquid components. If necessary, the peptide of the present invention to be detected can be separated and removed in advance from high molecular weight protein fractions using an antibody column, other adsorbent columns, or spin columns.

The peptides of the present invention can be detected in biological samples by, for example, subjecting the biological sample to various molecular weight measurement methods, such as gel electrophoresis, various separation and purification methods (e.g., ion exchange chromatography, hydrophobic chromatography, affinity chromatography, reverse phase chromatography, etc.), surface plasmon resonance, ionization methods (e.g., electron impact ionization, field desorption, secondary ionization, fast atom bombardment, matrix-assisted laser desorption ionization (MALDI), electrospray ionization, etc.), and mass spectrometry (e.g., dual-phase ionization, etc.). This can be done by, but is not limited to, subjecting the peptide to a method combining a focusing mass spectrometer, a quadrupole mass spectrometer, a time-of-flight mass spectrometer, a Fourier transform mass spectrometer, an ion cyclotron mass spectrometer, an immunomass spectrometer, a mass spectrometer using a stable isotope peptide as an internal standard, an MS-MS mass spectrometer using a stable isotope-labeled fragment ion as an internal standard, an immunomicroscope, a mass microscope, etc.) and detecting a band that matches the molecular weight or mass of the peptide, or a fragment ion, spot, or peak of the peptide.

A method of preparing an antibody against the peptide of the present invention and detecting the peptide by ELISA, RIA, immunochromatography, surface plasmon resonance, Western blotting, immunomass spectrometry, various immunoassays, or immunomicroscopy can also be preferably used. Furthermore, a hybrid detection method of the above methods is also effective. The epitope recognized or bound by the antibody against the present invention has a partial amino acid region (antigenic determinant) having the antigenicity or immunogenicity of the peptide of the present invention. The epitope usually consists of at least 5 amino acids, preferably at least 7 amino acids, and more preferably at least 10 amino acids.

One particularly preferred measurement method in the detection or determination method of the present invention is a method in which a test sample is brought into contact with the surface of a plate used for time-of-flight mass spectrometry, and the mass of the component captured on the surface of the plate is measured with a time-of-flight mass spectrometer. Any plate compatible with a time-of-flight mass spectrometer may be used as long as it has a surface structure (e.g., functionalized glass, Si, Ge, GaAs, GaP, _SiO2 , _SiN4 , modified silicon, various gel or polymer coatings) that can efficiently adsorb the peptide of the present invention to be detected.

In a preferred embodiment, the support used as the mass spectrometry plate is a substrate coated with a thin layer of polyvinylidene difluoride (PVDF), nitrocellulose or silica gel, particularly preferably PVDF (see WO 2004/031759). Such substrates are not particularly limited as long as they are used in mass spectrometry plates, and examples thereof include insulators, metals, conductive polymers, and composites thereof. A preferred example of such a mass spectrometry plate coated with a thin layer of PVDF is the Blot Chip (registered trademark) manufactured by Protosera Co., Ltd. Alternatively, the mass spectrometry plate can be prepared by a known method by coating the support surface by known means such as painting, spraying, vapor deposition, immersion, printing, sputtering, etc. In addition, the method of mass spectrometry of molecules on a mass spectrometry plate is known per se (for example, WO 2004/031759). The method described in WO 2004/031759 can be used with appropriate modifications as necessary.

The transfer of a test sample to a plate (support) for mass spectrometry is carried out by subjecting a biological sample from a subject to SDS-polyacrylamide gel electrophoresis or isoelectric focusing, either untreated or after removing and concentrating high molecular weight proteins using an antibody column or other method, and then contacting the gel with a plate for transfer (blotting). The transfer method itself is known, and electrical transfer is preferably used. As a buffer for electrical transfer, it is preferable to use a known buffer with a pH of 7 to 9 and a low salt concentration (e.g., Tris buffer, phosphate buffer, borate buffer, acetate buffer, etc.).

By performing mass spectrometry on the molecules in the test sample captured on the support surface by the above method, the presence and amount of the peptide of the present invention, which is the target molecule, can be identified from the mass information. Mass spectrometry can also be performed using Protosera's BLOTCHIP (registered trademark)-MS system, which can perform everything from electrophoresis to mass spectrometry. It is also possible to use any program to compare the information from the mass spectrometer with mass spectrometry data from biological samples from non-affected individuals, patients after treatment (follow-up), or healthy individuals, and output the information as differential information. Such programs are well known, and it will be understood that a person skilled in the art can easily construct or modify such programs using known information processing techniques.

To obtain highly accurate mass spectrometry results, analysis is performed using a mass spectrometer such as a triple quadrupole connected to a high-performance liquid chromatograph. A stable isotope-labeled peptide of the target molecule is synthesized and mixed with the test sample as an internal standard of known amount, and the peptide fraction is roughly purified using a reversed-phase solid support or the like. After being introduced into the high-performance liquid chromatograph, each separated peptide is ionized in a mass spectrometer, then fragmented in a collision cell, and the resulting peptide fragments are quantified by a multiple reaction monitoring method. Such peptide quantification can also be performed using ProtoKey (registered trademark) from Protosera Co., Ltd. In this case, actual measurement data with a CV value of 5% or less can be obtained by using a stable isotope-labeled peptide as an internal standard. Stable isotope-labeled peptides are obtained by replacing the original amino acid sequence positions with stable isotope-labeled amino acids purchased from suppliers such as Cambridge Isotope Laboratory (MA, USA) using existing synthesis methods (for example, solid-phase reaction using F-moc).

In the above detection by mass spectrometry, the peptide can be identified using tandem mass spectrometry (MS/MS). Examples of such identification methods include de novo sequencing, which determines the amino acid sequence by analyzing MS/MS spectra, and a method of identifying the peptide by performing a database search using partial sequence information (mass tag) contained in the MS/MS spectrum. In addition, by using the MS/MS method, the amino acid sequence of the peptide of the present invention can be directly identified, and the peptide can be synthesized in whole or in part based on the sequence information and used as an antigen for the following antibody.

The peptide of the present invention can also be measured using an antibody against the peptide. Thus, the present invention includes a method for detecting or determining pancreatic cancer using an antibody that specifically recognizes the peptide, a detection or determination agent for pancreatic cancer that contains such an antibody, and a detection or determination kit for pancreatic cancer that contains such an antibody. This method is particularly useful in that if an optimized immunoassay system is constructed and made into a kit, the peptide can be detected with high sensitivity and high accuracy without using a special device such as the mass spectrometer described above.

Antibodies against the peptides of the present invention can be prepared, for example, by isolating and purifying the peptides of the present invention from a biological sample derived from a patient expressing the peptides, and immunizing an animal with the peptides as an antigen. Alternatively, when the amount of peptide obtained is small, the peptides can be prepared in large quantities by well-known genetic engineering techniques such as amplifying a cDNA fragment encoding the peptide by RT-PCR, or the peptides of the present invention can be obtained using a cell-free transcription/translation system with such cDNA as a template. Furthermore, the peptides can also be prepared in large quantities by organic synthesis.

The antibody against the peptide of the present invention (hereinafter, sometimes referred to as "the antibody of the present invention") may be either a polyclonal antibody or a monoclonal antibody, and can be prepared by a well-known immunological technique. Moreover, the antibody includes not only a complete antibody molecule but also a fragment thereof, such as Fab, F(ab') ₂ , ScFv, and minibody.

For example, polyclonal antibodies can be obtained by administering the peptide of the present invention as an antigen together with a commercially available adjuvant (e.g., complete or incomplete Freund's adjuvant) to an animal subcutaneously or intraperitoneally about 2 to 4 times at intervals of 2 to 3 weeks, collecting whole blood after the final immunization, and purifying the antiserum. Animals to which the antigen is administered include mammals from which the desired antibodies can be obtained, such as rats, mice, rabbits, goats, sheep, horses, guinea pigs, and hamsters.

The detection or determination method of the present invention using an antibody is not particularly limited, and any measurement method may be used as long as the amount of antibody, antigen, or antibody-antigen complex corresponding to the amount of antigen in the test sample is detected by chemical or physical means and calculated from a standard curve prepared using a standard solution containing a known amount of antigen. For example, nephelometry, competitive method, immunometric method, sandwich method, etc. are preferably used. In the measurement, the antibody or antigen may be bound to a labeling agent such as a radioisotope, an enzyme, a fluorescent substance, or a luminescent substance. Furthermore, a biotin-avidin system may be used to bind the antibody or antigen to the labeling agent. These individual immunological measurement methods can be applied to the quantitative method of the present invention by the ordinary skill of a person skilled in the art.

Because the peptides of the present invention are made of proteolytic products, various molecules such as undegraded proteins and similar peptides with a common cleavage site may affect the measured values. Therefore, in the first step, the biological sample is purified by immunoaffinity purification using an antibody, and in the second step, the fraction bound to the antibody is subjected to mass spectrometry to identify and quantify based on precise mass, a so-called immunomass spectrometry method can be used (see, for example, Rapid Commun. Mass Spectrom. 2007, 21(3): 352-358). With immunomass spectrometry, both undegraded proteins and similar peptides are completely separated by a mass spectrometer, making it possible to quantify with high specificity and sensitivity based on the exact mass of the biomarker.

Alternatively, another detection or determination method of the present invention using the antibody of the present invention includes a method in which the antibody is immobilized on the surface of a chip that is compatible with a mass spectrometer as described above, a test sample is brought into contact with the antibody on the chip, the biological sample components captured by the antibody are subjected to mass spectrometry, and a peak corresponding to the mass of the marker peptide recognized by the antibody is detected.

When the level of the peptide of the present invention in a sample derived from a subject measured by any of the above methods varies significantly compared to the peptide level in a control sample derived from a non-pancreatic cancer patient, a patient after treatment, or a healthy individual, it can be determined that the subject is highly likely to be suffering from pancreatic cancer.

The peptides of the present invention can be used individually as markers for detecting pancreatic cancer, but by combining two or more types, the sensitivity (detection rate) and specificity (detection rate of no disease) can be further increased.

Examples of detection methods when two or more peptides are used as markers include (1) a method in which a patient is determined to have pancreatic cancer if the levels of all peptides to be measured change significantly, and a method in which a patient is determined to not have pancreatic cancer if the levels of any of the peptides do not change significantly; (2) a method in which a patient is determined to not have pancreatic cancer if the levels of all peptides to be measured do not change significantly, and a method in which a patient is determined to have pancreatic cancer if the levels of any of the peptides change significantly; (3) a method in which a patient is determined to have pancreatic cancer if, for example, 2 to (n-1) or more peptides out of n peptides to be measured change significantly, and further a method in which each peptide is weighted; and (4) machine learning methods such as bagging, boosting, and random forest. In particular, it is preferable to use multivariate logistic regression analysis, which is an analysis method that can handle multiple marker peptides as one marker set. In this case, the number of peptides used as markers is not particularly limited, but is preferably 2 or more, more preferably 3, more preferably 4 or more, and more preferably 5 or more.

In one embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2. Such a combination of peptides increases the detection accuracy.

In one embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and at least one peptide selected from the peptides consisting of the amino acid sequences represented by SEQ ID NOs: 2 to 10. Such a combination of peptides increases the detection accuracy.

In one embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2 and at least one peptide selected from the peptides consisting of the amino acid sequences represented by SEQ ID NOs: 1, 3 to 10. Such a combination of peptides increases the detection accuracy.

In one embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, and at least one peptide selected from peptides consisting of the amino acid sequences represented by SEQ ID NOs:3 to 10. Such a combination of peptides increases the detection accuracy.

In a preferred embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:6, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:8. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, which are present in the subject's biological sample. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides used in the above analysis include a peptide having the amino acid sequence represented by SEQ ID NO: 1 and/or a peptide having the amino acid sequence represented by SEQ ID NO: 2 in the subject's biological sample, at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs: 3 to 8, and a peptide having the amino acid sequence represented by SEQ ID NO: 9. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:9, which are present in the subject's biological sample. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides used in the above analysis include a peptide having the amino acid sequence represented by SEQ ID NO:1 and/or a peptide having the amino acid sequence represented by SEQ ID NO:2 in the subject's biological sample, at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:3-8, and a peptide having the amino acid sequence represented by SEQ ID NO:10. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the peptides used in the above analysis include a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 5, a peptide consisting of the amino acid sequence represented by SEQ ID NO: 7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 10, which are present in the subject's biological sample. Such a combination of peptides significantly increases the detection accuracy.

In this application, we constructed a multivariate logistic regression model of candidate peptides identified by mass spectrometry using the maximum likelihood method, and found that it is possible to detect or diagnose pancreatic cancer with a high reliability, with a high ROC area under the curve (AUC) (greater than 0.9).

The number of peptides to be detected or measured is preferably the number at which the AUC in the testing method of the present invention exceeds a certain threshold value. The threshold value is preferably 0.85, more preferably 0.9. For example, according to a logistic regression model that is a function of the blood concentrations of a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:6, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:8, the area under the curve (AUC) of the ROC plotting sensitivity against specificity exceeds 0.85, making it possible to detect pancreatic cancer with extremely high accuracy.

A logistic regression model that is a function of the blood concentrations of multiple peptides having the amino acid sequences represented by SEQ ID NOs: 1 to 10 brings the AUC closer to 1, enabling pancreatic cancer to be detected with high accuracy.

Note that while the peptide of the present invention alone may have an AUC of 0.85 or less, such a peptide is still effective as a biomarker for detecting or determining pancreatic cancer as long as it can be used to detect or determine pancreatic cancer with an AUC of more than 0.85, for example by combining multiple peptides of the present invention.

The detection method of the present invention can also be carried out by collecting biological samples from a patient in a time series and examining the change over time in the expression of the peptide of the present invention in each sample. The intervals at which biological samples are collected are not particularly limited, but it is desirable to sample as frequently as possible without impairing the patient's QOL. For example, when using plasma or serum as the sample, it is preferable to collect blood every about one day to about one year.

Furthermore, the above-mentioned method for detecting pancreatic cancer by time-series sampling can be used to evaluate the effectiveness of treatment for the disease when a treatment for the disease is administered to a patient who is a subject between the previous sampling and the current sampling. That is, for samples sampled before and after the treatment, if it is determined that the state after the treatment shows a decrease or increase in peptides (improvement of the pathology) compared to the state before the treatment, it can be evaluated that the treatment was effective. On the other hand, if it is determined that the state after the treatment shows no decrease or increase in peptides (improvement of the pathology) compared to the state before the treatment, or that the condition has worsened, it can be evaluated that the treatment was ineffective.

Furthermore, the above-mentioned testing method for detecting pancreatic cancer by time-series sampling can be used to evaluate the preventive effect of measures to reduce the risk of developing pancreatic cancer, such as taking health foods, quitting smoking, exercise therapy, and isolating from harmful environments. That is, for samples sampled before and after the implementation of the risk reduction measures, if it is determined that the state after the implementation shows no decrease or increase in peptides (onset or progression of the disease) compared to the state before the implementation, it can be evaluated that the implementation of the measures was effective. On the other hand, if the state after treatment shows no increase or decrease in peptides (improvement of the disease) compared to the state before the treatment, or if it is determined that the disease has worsened, it can be evaluated that the implementation of the measures was ineffective.

Therefore, the peptides and methods of the present invention can be used not only as markers for diagnosing or detecting pancreatic cancer, but also as markers for predicting the prognosis of pancreatic cancer and for assessing the effectiveness of treatment. In other words, the peptides and methods of the present invention can be used to screen drug discovery target molecules for the treatment of pancreatic cancer, and/or as companion diagnostics for selecting patients (responders) or adjusting the dosage of therapeutic drugs.

The peptides and methods of the present invention can also be used in screening methods for substances. In this case, substances include foods such as health foods and FOSHU products that prevent pancreatic cancer at the pre-disease stage, markers that diagnose or detect pancreatic cancer, and pharmaceuticals such as therapeutic drugs that treat pancreatic cancer after the onset of the disease.

For example, a method for screening for a substance effective in treating pancreatic cancer according to one embodiment of the present invention includes administering a test substance to a non-human animal (e.g., mouse, rat, guinea pig, rabbit, etc.), particularly a non-human animal model of pancreatic cancer, and detecting a change in the level (concentration, amount, or signal intensity) of the peptide of the present invention in a biological sample (particularly serum, plasma) derived from the non-human animal after administration of the test substance.

If the peptide levels in the biological sample change in a direction approaching the values of healthy subjects compared to the levels before administration of the test substance (a decrease in peptides consisting of the amino acid sequences represented by SEQ ID NOs: 1, 3, 5, 6, 8, and 9, and an increase in peptides consisting of the amino acid sequences represented by SEQ ID NOs: 2, 4, 7, and 10), this indicates that the test substance is an effective candidate substance for treating pancreatic cancer, and the candidate substance can be selected.

Alternatively, if the peptide levels in the biological sample remain the same or change away from those of healthy subjects compared to before administration of the test substance (an increase in peptides consisting of the amino acid sequences represented by SEQ ID NOs: 1, 3, 5, 6, 8, and 9, and a decrease in peptides consisting of the amino acid sequences represented by SEQ ID NOs: 2, 4, 7, and 10), this indicates that the test substance is not an effective candidate substance for the treatment of pancreatic cancer, and it can be determined as such.

Alternatively, screening may involve obtaining a multivariate logistic regression equation using the measured values of two or more peptides in a biological sample (particularly serum or plasma), substituting the measured values of two or more peptides before administration of the test substance and the measured values of two or more peptides after administration of the test substance into the regression equation, and comparing the obtained values to determine whether there has been a change toward the values of healthy subjects or a change away from the values of healthy subjects.

The present invention includes a pancreatic cancer detection kit that includes an antibody against one or more peptides of the present invention.

In one embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide having an amino acid sequence represented by SEQ ID NO:1 and an antibody against a peptide having an amino acid sequence represented by SEQ ID NO:2.

In one embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide having an amino acid sequence represented by SEQ ID NO:1 and at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:2 to 10.

In one embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide having an amino acid sequence represented by SEQ ID NO:2 and at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:1, 3 to 10.

In one embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide having an amino acid sequence represented by SEQ ID NO:1, a peptide having an amino acid sequence represented by SEQ ID NO:2, and at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:3 to 10.

In a preferred embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:6, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:8.

In a preferred embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide in a biological sample from a subject, the peptide having an amino acid sequence represented by SEQ ID NO:1, the peptide having an amino acid sequence represented by SEQ ID NO:2, the peptide having an amino acid sequence represented by SEQ ID NO:3, the peptide having an amino acid sequence represented by SEQ ID NO:5, and the peptide having an amino acid sequence represented by SEQ ID NO:7. Such a combination of peptides significantly increases the detection accuracy.

In a preferred embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide having an amino acid sequence represented by SEQ ID NO:1 and/or a peptide having an amino acid sequence represented by SEQ ID NO:2, at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:3 to 8, and a peptide having an amino acid sequence represented by SEQ ID NO:9 in the subject's biological sample.

In a preferred embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:9 in the subject's biological sample.

In a preferred embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide having an amino acid sequence represented by SEQ ID NO:1 and/or a peptide having an amino acid sequence represented by SEQ ID NO:2, at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:3 to 8, and a peptide having an amino acid sequence represented by SEQ ID NO:10 in the subject's biological sample.

In a preferred embodiment, the antibodies against each of the one or more peptides include antibodies against a peptide having an amino acid sequence represented by SEQ ID NO:1, a peptide having an amino acid sequence represented by SEQ ID NO:5, a peptide having an amino acid sequence represented by SEQ ID NO:7, and a peptide having an amino acid sequence represented by SEQ ID NO:10 in a biological sample from a subject.

In a preferred embodiment, the present invention includes a pancreatic cancer detection agent that contains an antibody against each of one or more peptides of the present invention as a detection reagent. The antibody against each of one or more peptides can be the same as the antibody contained in the pancreatic cancer detection kit.

The present invention encompasses a computer-implemented method for determining the possibility of a subject having pancreatic cancer, comprising the steps of: acquiring quantitative data on at least one or more peptides selected from a peptide having an amino acid sequence represented by SEQ ID NO: 1 of the present invention and/or a peptide having an amino acid sequence represented by SEQ ID NO: 2, and a peptide having an amino acid sequence represented by SEQ ID NO: 3 to 10, in a biological sample from the subject; and applying the acquired data to a multivariate logistic regression model that is a function of the two, three, or four or more peptides, to obtain a predicted value of the possibility of the subject having pancreatic cancer. Here, the quantitative data of the peptide refers to quantitative measurements such as the expression level and blood concentration of the peptide measured using mass spectrometry or an antibody against the peptide.

In a preferred embodiment, the present invention encompasses a computer-implemented method for determining the possibility of pancreatic cancer in a subject, the method comprising the steps of obtaining quantitative data on a peptide consisting of the amino acid sequence represented by SEQ ID NO:1 and/or a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, and at least one or more peptides selected from peptides consisting of the amino acid sequences represented by SEQ ID NOs:3 to 10, in a biological sample from the subject, and applying the obtained data to a multivariate logistic regression model that is a function of the peptides, to obtain a predicted value for the possibility of pancreatic cancer in the subject.

In one embodiment, the peptides used in the above method include a peptide having an amino acid sequence represented by SEQ ID NO:1 and a peptide having an amino acid sequence represented by SEQ ID NO:2.

In one embodiment, the peptide used in the above method includes a peptide having an amino acid sequence represented by SEQ ID NO:1 and at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:2 to 10.

In one embodiment, the peptide used in the above method includes a peptide having an amino acid sequence represented by SEQ ID NO:2 and at least one peptide selected from peptides having amino acid sequences represented by SEQ ID NOs:1, 3 to 10.

In one embodiment, the peptides used in the above method include at least one peptide selected from a peptide having an amino acid sequence represented by SEQ ID NO:1, a peptide having an amino acid sequence represented by SEQ ID NO:2, and a peptide having an amino acid sequence represented by SEQ ID NO:3 to 10.

In a preferred embodiment, the peptides used in the above method include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:6, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:8.

In a preferred embodiment, the peptides used in the above method include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:7 in the subject's biological sample.

In a preferred embodiment, the peptides used in the above method include a peptide having the amino acid sequence represented by SEQ ID NO:1 and/or a peptide having the amino acid sequence represented by SEQ ID NO:2 in the subject's biological sample, at least one peptide selected from peptides having the amino acid sequences represented by SEQ ID NOs:3 to 8, and a peptide having the amino acid sequence represented by SEQ ID NO:9.

In a preferred embodiment, the peptides used in the above method include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:9 in the subject's biological sample.

In a preferred embodiment, the peptides used in the above method include a peptide having the amino acid sequence represented by SEQ ID NO:1 and/or a peptide having the amino acid sequence represented by SEQ ID NO:2 in the subject's biological sample, at least one peptide selected from peptides having the amino acid sequences represented by SEQ ID NOs:3 to 8, and a peptide having the amino acid sequence represented by SEQ ID NO:10.

In a preferred embodiment, the peptides used in the above method include a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:10 in the subject's biological sample.

The above computer-implemented method may further include a step of determining the possibility of the subject having pancreatic cancer based on the predicted value after the predicted value is obtained. For example, if the obtained predicted value exceeds a certain threshold, the subject is determined to have a high possibility of having pancreatic cancer. The threshold is, for example, a value greater than the average or median of predicted values measured from peptides of multiple healthy subjects, or the average or median of predicted values measured from pancreatic cancer patients.

The present invention will be explained in more detail below with reference to examples, but it goes without saying that the present invention is not limited to these.

The disclosures of all patent applications and literature cited herein are hereby incorporated by reference in their entireties.

Example 1 Identification of pancreatic cancer marker peptides 1. Subjects and blood collection At Toyama University, 6 mL of blood was collected from 118 pancreatic cancer patients and 118 healthy subjects. The collected blood was left to stand for 0.5 to 1.0 hours, then centrifuged at 3,000 rpm for 10 minutes at room temperature to obtain serum. The supernatant was stored separately at -80°C until use.

The diagnostic criteria for pancreatic cancer were cases in which a tumor was found in the pancreas through imaging tests, including abdominal contrast CT scans, abdominal MRI scans, and endoscopic ultrasound scans, and pancreatic cancer was diagnosed through pathological examination (diagnosis was made using surgical specimens in surgical cases, and biopsy specimens obtained by endoscopic ultrasound fine-needle aspiration in cases in which surgery was not performed).

2. BLOTCHIP® Mass Spectrometry Mass spectrometric peptide analysis of serum was performed using the one-step direct transfer technology BLOTCHIP® mass spectrometry, a rapid quantitative method for peptidome profiling (Biochem. Biophys. Res. Commun. 2009;379(1):110-114).

First, serum samples were subjected to sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis (PAGE) to separate peptides from proteins. Next, the peptides in the gel were electrotransferred to a BLOTCHIP (registered trademark) (Protosera Co., Ltd., Settsu City, Osaka, Japan). After the transfer was completed, the surface of the chip was rinsed with ultrapure water, and a matrix (α-cyano-4-hydroxycinnamic acid, Sigma-Aldrich Co., Missouri, USA) was directly applied to the BLOTCHIP (registered trademark). Mass spectrometry was then performed using an UltrafleXtreme MALDI-TOF/TOF mass spectrometer (Bruker, Massachusetts, USA) in linear mode as described in Proteomics 2011, 11:2727-2737 to obtain a peptide profile.

3. Statistical analysis Samples were analyzed four times by BLOTCHIP® mass spectrometry. To find more statistically significant peaks, the four data were used as independent data, and the p-value of the Wilcoxon test was calculated using the analysis software ClinProTools 3.0 (Bruker). A p-value of 0.05 or less was considered to be significant.

The statistical analysis software R (R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.r-project.org/) was used to calculate the p-value of the Wilcoxon test, and the diagnostic performance of peptides was evaluated using the average value of four data per sample, and useful biomarker peptides with high diagnostic performance were discovered. The statistical analysis software R (R Core Team (2020) was used to construct the model.

A receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic ability of the constructed model. The R package "Epi package" (A package for statistical analysis in epidemiology, Version 2.47, http://cran.r-project.org/web/packages/Epi/index.html) was used. The area under the curve was calculated. The optimal cutoff value for diagnosis was determined according to Youden's index in Cancer 1950;3:32-35.

4. Identification of peptides Each peptide was extracted with 80% v/v acetonitrile (ACN) in water containing 0.1% trifluoroacetic acid using a Sep-Pak C18 solid-phase extraction cartridge (Waters Corporation, Milford, MA, USA). The eluate was concentrated to less than 100 μL using a CC-105 centrifugal concentrator (Tommy Seiko Co., Ltd., Tokyo, Japan). The solution was then diluted with 400 μL of 2% v/v ACN solution containing 0.065% TFA (referred to as eluent A) and applied to an AKTA purification system (GE Healthcare UK Ltd, Buckinghamshire, UK) equipped with a C18 silica column (XBridge Shield RP18 2.5 mL; Waters). The eluate was divided into 24 fractions (1 mL each) using a linear gradient of 0–100% 80% v/v ACN solution containing 0.05% TFA against eluent A at a flow rate of 1.0 mL/min. Each fraction was concentrated to less than 10 μL using a CC-105 centrifugal concentrator, and peptide sequences were analyzed using MALDI-TOF/TOF (ultrafleXtreme; Bruker) and LC-MS/MS (Orbitrap Eclipse; Thermo Fisher Scientific Inc, Waltham, MA, USA).

(result)
1. Identification of biomarker peptides for pancreatic cancer Peptide analysis of 118 serum samples from pancreatic cancer patients and 118 serum samples from healthy subjects was performed using BLOTCHIP (registered trademark) mass spectrometry. Mass spectrum data obtained from each peptidome profile was stored in a database. After all MS measurements were completed, differential analysis of the two groups was performed using the analysis software ClinProTools3.0. As a result, the shapes of all peaks obtained by statistical analysis were visually inspected to remove noise, weak peaks, and faint peaks that appear randomly in MALDI-MS measurements.

Furthermore, MALDI-TOF/TOF and LC-MS/MS peptide sequencing analyses were performed on serum peptides partially purified by reverse phase chromatography. Nine peptides were ultimately identified (Table 1).

The AUC for the peptide of sequence number 1 is 0.681 even as a single marker, and the AUC for sequence number 2 is 0.669.

2. Amino Acid Sequence Analysis The amino acid sequences of the above peptides Nos. 1 to 8 were determined by peptide sequencing methods well known to those skilled in the art (Table 2). The following were confirmed: pyroglutamylation of the first Gln at the N-terminus of the peptide consisting of the amino acid sequence represented by SEQ ID NO:1; acetylation of the first serine in the peptide consisting of the amino acid sequence represented by SEQ ID NO:4; oxidation of the 16th methionine (the 398th from the N-terminus of α-1-antitrypsin protein) in the peptide consisting of the amino acid sequence represented by SEQ ID NO:5; oxidation of the 27th methionine (the 409th from the N-terminus of α-1-antitrypsin protein) in the peptide consisting of the amino acid sequence represented by SEQ ID NO:5; S-cysteinylation of the 9th cysteine (the 336th from the N-terminus of prothrombin protein) in the peptide consisting of the amino acid sequence represented by SEQ ID NO:6; oxidation of the 9th methionine (the 536th from the fibrinogen α chain protein) in the peptide consisting of the amino acid sequence represented by SEQ ID NO:7; and oxidation of the 28th methionine (the 603rd from the fibrinogen α chain protein) in the peptide consisting of the amino acid sequence represented by SEQ ID NO:8.

Example 2 Multivariate logistic regression analysis A multivariate logistic regression equation was obtained using the average signal intensity values of each peptide of SEQ ID NO: 1, 2, 5, 6, 7, and 8 of pancreatic cancer patients and healthy subjects in the statistical analysis of Example 1, and the median of the predicted probability was calculated to examine whether there was a statistically significant difference between the two groups of pancreatic cancer patients and healthy subjects (Table 3). The peptide of SEQ ID NO: 5 was an oxidized form in which the 16th and 27th methionines were oxidized. The predicted probability was also shown as a risk index in a scatter plot and a box plot (Figure 1).

As shown in Table 3, the value obtained by substituting the average signal intensity values of six types of biomarker peptides into the regression equation was significantly increased by 6.03 times in the pancreatic cancer patient group (PAC) compared to the healthy subject group (control). In addition, the AUC was 0.923, indicating that these peptides are useful as biomarkers. When the oxidized form in which the 16th methionine is oxidized was used instead of the oxidized form in which the 16th and 27th methionines are oxidized as the peptide of sequence number 5, the signal intensity ratio of the pancreatic cancer patient group (PAC) to the healthy subject group (control) was 5.24 times, and the AUC was 0.913 (data not shown).

Example 3 Multivariate Logistic Regression Analysis For combinations of multiple peptides from among the total 9 and 8 types of biomarker peptides identified in Example 1, the levels of each peptide were measured in the serum of subjects by proteome analysis using ProtoKey (registered trademark). In addition, peptide number 9 was added as a biomarker peptide for pancreatic cancer (Tables 4 and 5). Using this measurement value, a multivariate logistic regression equation for predicted probability was obtained, and the median of predicted probability was calculated, and a statistically significant difference was examined between the two groups, the pancreatic cancer patient group and the healthy subject group (Table 6). In Test 1, peptides number 1, 2, 3, 5, and 7 were used. In Test 2, peptides number 1, 2, 3, 5, 7, and 9 were used. Note that although multiple oxidized forms were detected for peptide number 5 by BLOTCHIP (registered trademark) mass spectrometry, no oxidized form was detected by measurement using ProtoKey (registered trademark), and only the unoxidized peptide was measured.

As shown in Table 6, in Test 1, the value obtained by substituting the blood concentrations of five types of biomarker peptides into the regression equation was significantly increased by 4.88 times in the pancreatic cancer patient group (PAC) compared to the healthy subject group (control). In addition, the AUC was 0.910, which showed that pancreatic cancer can be diagnosed quickly and exclusively with very high diagnostic performance, and that these peptides are useful as biomarkers. In Test 2, the value obtained by substituting the blood concentrations of six types of biomarker peptides, which further include peptide number 9 in Test 1, into the regression equation was significantly increased by 5.47 times in the pancreatic cancer patient group (PAC) compared to the healthy subject group (control). In addition, the AUC was 0.915, and the diagnostic accuracy was further improved. Furthermore, by using the peptides of Test 1 and Test 2, not only could it be possible to distinguish between controls and pancreatic cancer patients, but it was also possible to distinguish between pancreatic cancer and colon cancer (data not shown). It is understood that these markers are useful for the detection, determination, and/or diagnosis of pancreatic cancer.

Example 4 Multivariate logistic regression analysis For combinations of multiple peptides from among the total 9 and 8 types of biomarker peptides identified in Example 1, the levels of each peptide were measured in the serum of subjects by proteome analysis using ProtoKey (registered trademark). In addition, peptide number 10 was added as a biomarker peptide for pancreatic cancer (Tables 7 and 8). A multivariate logistic regression equation for predicted probability was obtained using the measured values of SEQ ID NOs: 1, 5, 7, and 10, and the median of predicted probability was calculated to examine whether there was a statistically significant difference between the two groups, the pancreatic cancer patient group and the healthy subject group (Table 9).

As shown in Table 9, the values obtained by substituting the blood concentrations of the four types of biomarker peptides into the regression equation were significantly increased by 2.88 times in the pancreatic cancer patient group (PAC) compared to the healthy subject group (control). In addition, the AUC was 0.865, which showed that pancreatic cancer can be diagnosed quickly and with very high diagnostic performance, and that these peptides are useful as biomarkers.

The results of Examples 1 to 4 above suggest that the predicted values obtained from the blood concentrations of these peptide markers using a multivariate logistic regression model are useful for determining pancreatic cancer, determining pancreatic cancer patients for whom therapeutic drugs are effective (responders), determining which therapeutic drugs are effective for individual pancreatic cancer patients (companion diagnostic method), determining the effectiveness of prevention and treatment, testing methods for early diagnosis, testing methods for early treatment, and screening methods for substances.

Claims (15)

A method for detecting pancreatic cancer in a subject, comprising measuring at least one of a peptide having an amino acid sequence represented by SEQ ID NO:1 and a peptide having an amino acid sequence represented by SEQ ID NO:2 in a biological sample from the subject. The method according to claim 1, further comprising measuring one or more peptides selected from the group consisting of a peptide having an amino acid sequence represented by SEQ ID NO:3, a peptide having an amino acid sequence represented by SEQ ID NO:4, a peptide having an amino acid sequence represented by SEQ ID NO:5, a peptide having an amino acid sequence represented by SEQ ID NO:6, a peptide having an amino acid sequence represented by SEQ ID NO:7, a peptide having an amino acid sequence represented by SEQ ID NO:8, a peptide having an amino acid sequence represented by SEQ ID NO:9, and a peptide having an amino acid sequence represented by SEQ ID NO:10. The method according to claim 1, which is a method for detecting pancreatic cancer in a subject, comprising measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO: 1 and a peptide consisting of the amino acid sequence represented by SEQ ID NO: 2 in a biological sample from the subject. The method according to claim 1, which is a method for detecting pancreatic cancer in a subject, comprising measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:6, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:8 in a biological sample from the subject. The method according to claim 1, which is a method for detecting pancreatic cancer in a subject, includes measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:7 in a biological sample from the subject. The method according to claim 1, which is a method for detecting pancreatic cancer in a subject, includes measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:2, a peptide consisting of the amino acid sequence represented by SEQ ID NO:3, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:9 in a biological sample from the subject. The method according to claim 1, which is a method for detecting pancreatic cancer in a subject, comprising measuring a peptide consisting of the amino acid sequence represented by SEQ ID NO:1, a peptide consisting of the amino acid sequence represented by SEQ ID NO:5, a peptide consisting of the amino acid sequence represented by SEQ ID NO:7, and a peptide consisting of the amino acid sequence represented by SEQ ID NO:10 in a biological sample from the subject. The method according to any one of claims 1 to 7, comprising subjecting a biological sample to mass spectrometry. The method according to any one of claims 1 to 7, wherein the biological sample is a body fluid selected from the group consisting of blood, plasma, serum, saliva, urine, cerebrospinal fluid, bone marrow fluid, pleural fluid, peritoneal fluid, synovial fluid, sweat, tears, aqueous humor, vitreous humor, and lymphatic fluid. The method according to any one of claims 1 to 7, wherein the detection of pancreatic cancer is a method for determining pancreatic cancer, determining the preventive effect of pancreatic cancer, determining the therapeutic effect of pancreatic cancer, determining which pancreatic cancer patients are responsive to a therapeutic drug, determining which therapeutic drug is effective for an individual pancreatic cancer patient, a test method for diagnosing pancreatic cancer, or a test method for treating pancreatic cancer. A pancreatic cancer detection kit comprising an internal standard in which at least one of a peptide having an amino acid sequence represented by SEQ ID NO:1 and a peptide having an amino acid sequence represented by SEQ ID NO:2 is labeled with a stable isotope. A kit for detecting pancreatic cancer, comprising an antibody against at least one of a peptide having an amino acid sequence represented by SEQ ID NO:1 and a peptide having an amino acid sequence represented by SEQ ID NO:2. A detection agent for pancreatic cancer comprising, as a detection reagent, an antibody against at least one of a peptide having an amino acid sequence represented by SEQ ID NO:1 and a peptide having an amino acid sequence represented by SEQ ID NO:2. A computer-implemented method for determining the possibility of a subject having pancreatic cancer, the method including the steps of: acquiring quantitative data on at least one or more peptides selected from the peptide having the amino acid sequence represented by SEQ ID NO:1 of the present invention and/or the peptide having the amino acid sequence represented by SEQ ID NO:2, and the peptide having the amino acid sequence represented by SEQ ID NO:3 to 10, in a biological sample from the subject; and applying the acquired data to a multivariate logistic regression model that is a function of the two, three, or four or more peptides, to obtain a predicted value of the possibility of the subject having pancreatic cancer. A biomarker for detecting pancreatic cancer comprising at least one of a peptide having an amino acid sequence represented by SEQ ID NO:1 and a peptide having an amino acid sequence represented by SEQ ID NO:2.