Inspection method, inspection device, and inspection program

Abstract

Provided is a highly accurate method for examining a disease without imposing an excessive burden in a medical setting. The method for conducting examination of a disease using disease markers includes a step for acquiring marker data showing the measurement results of disease markers measured in a body fluid sample collected from a subject and preparation data showing the preparation conditions of the body fluid sample; a step for correcting the marker data using the preparation data; and a step for assessing whether or not the subject has contracted the disease based on the corrected marker data.

Description

[Technical field]

[0001] The present invention relates to a testing method, a testing device, and a testing program for testing for a disease using a disease marker. [Background technology]

[0002] Analysis of proteins, DNA, and RNA contained in blood for the purpose of testing for diseases has been conducted since the 1960s. In particular, analysis of miRNA has become more common since around 2007. In recent years, efforts are being made around the world to determine the presence or absence of cancer and other diseases from the expression profile of miRNA in serum.

[0003] In tests using these biomolecules (disease markers) that are related to disease onset and serve as indicators of disease onset, it is necessary to suppress fluctuations in the abundance of disease markers associated with sample collection. For example, Patent Document 1 discloses that after storing a serum sample at 4°C for 72 or 168 hours, the abundance of some miRNAs in the sample fluctuates significantly. For this reason, it is common to standardize protocols, such as by standardizing test conditions, including sample collection. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication WO2017 / 146033 Summary of the Invention [Problem to be solved by the invention]

[0005] Since sampling of subject samples is performed at each medical institution, the conditions may differ from institution to institution, and therefore, in order to prevent fluctuations in disease markers, we are forced to ask medical institutions for their cooperation in standardizing the conditions.

[0006] Furthermore, in the course of the study, the present inventors have found that not only when the relatively long storage time (72 hours or 168 hours) shown in Patent Document 1 is performed, but also when the serum is adjusted for a short time (for example, within 6 hours), the expression profile of miRNA can unexpectedly vary greatly. Therefore, in order to perform a test with high accuracy, it is necessary to exclude miRNAs that are prone to variation from the analysis target, or to request medical institutions to comply with a unified protocol or to sample under more detailed conditions in order to suppress the variation as much as possible. However, discarding data by excluding some miRNAs from the analysis target is a great loss in performing a test with high accuracy. In addition, compliance with strict protocols and sampling under more detailed conditions will impose an additional burden on the medical field, which may hinder the implementation or dissemination of the test.

[0007] Therefore, the present invention has been made in consideration of the above problems, and its object is to provide a highly accurate disease testing method that allows for a wide range of feasible sample collection conditions without placing an excessive burden on medical facilities. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems, the testing method of the present invention is a testing method for testing for a disease using a disease marker, and includes a data acquisition step of acquiring marker data indicating the measurement results of measuring a disease marker in a body fluid sample collected from a subject and preparation data indicating the preparation conditions of the body fluid sample, a correction step of correcting the acquired marker data using the acquired preparation data to obtain corrected marker data, and a discrimination step of discriminating the presence or absence of a disease in the subject based on the corrected marker data, where the corrected marker data is an estimate of the value of the marker data under specified preparation conditions of the same type as the indicator in the acquired preparation data.

[0009] In order to solve the above-mentioned problems, the testing device of the present invention is a testing device that performs disease testing using disease markers, and is equipped with a data acquisition unit that acquires marker data indicating the results of measuring disease markers in a body fluid sample collected from a subject and preparation data indicating the preparation conditions of the body fluid sample, and a correction unit that uses the acquired preparation data to correct the acquired marker data to acquire corrected marker data, where the corrected marker data is an estimate of the value of the marker data under specified preparation conditions of the same type as the indicator in the acquired preparation data. Effect of the Invention

[0010] According to one aspect of the present invention, by using preparation data indicating the sample preparation conditions in addition to marker data indicating the results of measuring disease markers in a sample, it is possible to perform highly accurate disease testing without placing an undue burden on medical facilities. [Brief description of the drawings]

[0011] [Figure 1] 1 is a functional block diagram showing a schematic configuration of an inspection device and a terminal device according to an embodiment of the present invention; [Diagram 2] FIG. 13 is a diagram showing an ROC curve in which the discrimination performance was confirmed using sample group 1. [Diagram 3] FIG. 13 is a diagram showing an ROC curve in which the discrimination performance was confirmed using sample group 2. [Figure 4] FIG. 13 shows a comparison of average values ​​obtained using sample group 5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] An embodiment of the present invention will be described below. The testing method in this embodiment is a method for testing a disease using a disease marker, and is a testing method for determining the presence or absence of a disease after measuring a disease marker in a body fluid sample using preparation data indicating preparation conditions of the body fluid sample and correcting the marker data.

[0013] 〔term〕 First, the terminology will be explained. In this specification, the term "body fluid sample" refers to a body fluid collected from a subject and used in a test. There are no particular limitations on the body fluid as long as it can be used in a test for measuring a disease marker, and examples of the body fluid include blood, serum, plasma, cerebrospinal fluid, urine, saliva, tears, tissue fluid, and lymphatic fluid. Among these, blood, serum, and plasma are preferably used.

[0014] The term "disease marker" refers to a biomolecule whose presence or abundance is related to a specific disease. As used herein, the term "disease marker" refers to a disease marker for a disease to be examined. Thus, the term "disease marker" as used herein refers to a biomolecule whose presence or abundance is known to be related to a disease to be examined. Examples of disease markers include DNA, RNA, and proteins. Among these, RNA is preferably used, and non-coding RNA (ncRNA) is more preferably used. ncRNA is broadly classified into small ncRNAs with a length of about 20 to 200 bases and long ncRNAs with a length of several hundred to several hundred thousand bases. Examples of ncRNA include transfer RNA, ribosomal RNA, small nuclear RNA, small nucleolar RNA, signal recognition complex RNA, miRNA, piRNA, long non-coding RNA, circular RNA, and non-translated regions of mRNA, and miRNA is particularly preferably used. Furthermore, the disease is not particularly limited as long as the presence of a disease marker is known, and examples thereof include cancer, dementia, hypertension, heart disease, brain disease, hepatitis, infectious diseases, and allergies. Examples of cancer include pancreatic cancer, biliary tract cancer, breast cancer, lung cancer, colon cancer, esophageal cancer, gastric cancer, liver cancer, prostate cancer, bladder cancer, brain tumor, blood cancer, ovarian cancer, and uterine cancer, and examples thereof may include pancreatic cancer and biliary tract cancer.

[0015] In this specification, the term "marker data" refers to data indicating the presence or absence or the amount of one or more disease markers obtained by measuring a disease marker in a body fluid sample. When the actual detection target is DNA, RNA, or protein, the "abundance" can be rephrased as "expression amount". When used with respect to a disease marker, "measure" can be rephrased as "detect". The marker data in this embodiment is not limited to the result obtained by measuring one specific disease marker, but may include multiple measurement results or numerical data corresponding to each disease marker obtained by measuring multiple disease markers. The measurement results of multiple disease markers may be the result of simultaneously measuring each disease marker, or may be the result of measuring each disease marker independently. When multiple disease markers are used, the number is not limited, and may be, for example, 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 15 or more, 20 or more, 30 or more, or 40 or more. The measurement means for obtaining the marker data can be appropriately selected depending on the biomolecule to be measured and the required data. Examples of the measurement means include microarrays, various PCRs including qRT-PCR, various sequencings including next-generation sequencing, and ELISA. Among them, it is preferable to use a microarray, from the viewpoint that a plurality of disease markers can be measured simultaneously and a highly accurate test can be performed by using a plurality of disease markers. When converting the measurement results into numerical data, the conversion method is not particularly limited as long as a correlation is observed between the measurement results and the numerical data.

[0016] As used herein, "subject" refers to mammals such as primates, including humans and chimpanzees, pets such as dogs and cats, livestock animals such as cows, horses, sheep and goats, rodents such as mice and rats, and animals kept in zoos. A preferred subject is a human. A "healthy individual" is an individual of the same species as the subject of interest who is not affected by the disease of interest.

[0017] "Preparation data" refers to data indicating preparation conditions for a body fluid sample, and includes one or more pieces of information related to the body fluid sample other than the measurement results of disease markers obtained by measurement. In detail, examples of information included in the preparation data include information on the subject from which the body fluid sample was collected, information indicating the collection conditions of the body fluid sample, and information on the conditions of treatment performed on the body fluid sample before measuring disease markers in the body fluid sample. Differences in the subject or the condition of the subject, or differences in the collection or treatment conditions of the body fluid sample, can cause considerable variation in the profile of disease markers in the body fluid sample.

[0018] "Information on the subject" refers to information about the subject himself / herself that is not directly related to the collection of the bodily fluid sample, or information about the subject's physical condition at the time of collection of the bodily fluid sample. Such information includes, for example, information about the subject's race, biomolecular information that is not related to the disease being tested and that can be obtained from the subject's blood, dietary content and eating time on the day of or the day before the sample collection, the time from the subject's last meal to the collection of the bodily fluid, and, if the subject is taking medication, information about the body or physical condition, such as the type of medication taken or the time from taking the medication to the collection of the bodily fluid.

[0019] Examples of biomolecular information that can be obtained from the blood of a subject include the presence, abundance, and test values ​​of various biomolecules such as blood cells, proteins, lipids, and carbohydrates. Of these, information on biomolecules that are not known to correlate with the disease being examined may be used. For example, if the disease being examined is pancreatic cancer or biliary tract cancer, examples of biomolecular information that can be obtained from the blood of a subject and that is not known to correlate with the presence or absence of these diseases include blood cell volume (red blood cell volume, white blood cell volume, or platelet volume), protein volume such as PSA and CYFRA, and test values ​​such as HDL cholesterol volume and LDL cholesterol volume.

[0020] "Information indicating the collection conditions of the bodily fluid sample" refers to information regarding the conditions and the equipment used when collecting the bodily fluid. Such information includes the type of blood collection needle used when a needle is used to collect the bodily fluid, the type of blood collection tube used to collect the blood when the bodily fluid is blood or a blood-derived component, the type of coagulation promoter or coagulation inhibitor added to the blood collection tube, and the type of blood vessel when the blood was collected (capillary blood or venous blood), etc.

[0021] The "conditions of treatment applied to the body fluid sample" refer to the conditions of various treatments and operations applied to the body fluid sample before the measurement of the disease marker, and also include the conditions of storage of the body fluid sample. The conditions of storage include, for example, the time and temperature until freezing or transferring to a freezer, the temperature during freezing, the time of freezing, the time until decomposition enzymes in the body fluid sample, such as RNase, DNase, or Protease, are inactivated, the material of the sample storage container during freezing, and the use or non-use of packaging materials to cushion the impact of temperature changes at the start of freezing. In addition, the conditions of various treatments and operations that can be applied to the body fluid sample include the time from collection of the body fluid from the subject to centrifugation, and the centrifugal acceleration, when the body fluid is centrifuged or when the collected blood is centrifuged to obtain serum, which is the actual body fluid sample, etc. In addition, when the actual body fluid specimen is obtained by subjecting the collected body fluid to centrifugation, the time until the body fluid specimen is frozen or transferred to a freezer after centrifugation is also an example of the condition of the treatment applied to the body fluid specimen. Further, in order to measure a disease marker from a stored body fluid specimen, the time until the body fluid specimen is thawed from a frozen state and the temperature at which the body fluid specimen is thawed are also examples of the condition of the treatment applied to the body fluid specimen.

[0022] In addition, when the preparation conditions are non-numeric conditions such as race, type of blood collection tube, and type of medication taken, different numerical values ​​can be assigned to each of the expected specific candidates, and the regression equation can be created using these numerical values.

[0023] The preparation data may include at least one of the above information, and may include two or more, three or more, or four or more.

[0024] [Testing method] The testing method according to this embodiment includes the steps of acquiring marker data indicating the measurement results of disease markers in a body fluid sample collected from a subject and preparation data indicating the preparation conditions of the body fluid sample, correcting the acquired marker data using the acquired preparation data to obtain corrected marker data, and determining the presence or absence of a disease in the subject based on the corrected marker data.

[0025] (Marker data correction) The corrected marker data is an estimate of the value of the marker data under the same type of predetermined preparation conditions as the indicators in the acquired preparation data. Here, "the same type of predetermined preparation conditions as the indicators in the preparation data" means specific conditions that are the same type of preparation conditions as those used in the acquired preparation data and are arbitrarily set in advance. For example, when preparation data is acquired using "the time from collection of body fluid from the subject to centrifugation" as the preparation condition, it means "the time from collection of body fluid from the subject to centrifugation" that is arbitrarily set in advance, such as 0.5 hours.

[0026] The method of correcting the marker data is not particularly limited as long as it can estimate the value of the marker data under the same type of predetermined preparation conditions as the index in the acquired preparation data.Typically, a regression equation is created in advance by regression analysis using multiple preparation data of the same index and multiple marker data corresponding to each preparation data, and the regression equation is used.The use of the regression equation is not limited to the use of the obtained regression equation itself, and can include the use of numerical values ​​such as coefficients obtained by creating the regression equation.

[0027] The regression analysis is typically a linear regression analysis, and may be either a simple regression analysis or a multiple regression analysis, with the simple regression analysis being preferred.

[0028] (Specific embodiment 1 of marker data correction) Next, a specific embodiment of a method for correcting marker data will be described. As an example, a method for creating a regression equation as an estimation model will be described, using the time from collection of body fluid from a subject to centrifugation (hereinafter simply referred to as "time until centrifugation") as a preparation condition, and the signal intensity of a specific miRNA obtained using a microarray as marker data. Note that those skilled in the art can easily understand that correction can be performed in the same manner under other preparation conditions by referring to the following explanation.

[0029] The method for creating a regression equation in this embodiment includes the following steps: (a) miRNA signal intensities are obtained using multiple body fluid samples from healthy individuals with various centrifugation times.

[0030] (b) From each signal intensity obtained and the time to centrifugation for each corresponding body fluid sample, a regression equation was calculated, where y is the signal intensity and x is the time to centrifugation: y = a 0 x+b 0 Create a.

[0031] (c) The signal intensity obtained from the body fluid sample to be tested and the time until separation in the body fluid sample are calculated using the regression equation: y = a 0 x+b i By substituting y and x into each of the above, the b i Get the.

[0032] From the above, the regression equation specific to each body fluid sample: y = a 0 x+b i This regression equation becomes an estimation model for estimating the signal intensity of a specific body fluid sample under specified preparation conditions.

[0033] In step (a), in order to suppress variations in marker data caused by other factors, it is desirable that the conditions other than the time until centrifugation are as similar as possible for each body fluid sample. Therefore, in the above example, it is preferable to prepare multiple body fluid samples from the same body fluid before centrifugation, which differ only in the time until centrifugation, and to prepare multiple body fluid samples by processing them under the same post-preparation conditions (e.g., the time until storage at -80°C).

[0034] The signal intensity is corrected by substituting a predetermined standard time for x in the regression equation specific to each obtained body fluid sample. That is, the value obtained from the regression equation by substituting the standard time for x in the specific regression equation is the estimated value of the signal intensity at the standard time, i.e., the corrected signal intensity.

[0035] (Specific embodiment 2 of marker data correction) Next, a description will be given of another specific embodiment of the method for correcting marker data. In this embodiment, the marker data is corrected using an estimation model that uses only some of the coefficients of the regression equation obtained in the above-mentioned specific embodiment 1.

[0036] The regression equation, where y is the signal intensity and x is the time to centrifugation, is: y = a 0 x+b 0 The steps are the same as steps (a) and (b) in the above-mentioned specific embodiment 1 until the preparation of the sample. Here, x is the time until the centrifugation, so a 0 is the coefficient of change that represents the change in signal intensity per unit time required for centrifugation. Therefore, the standard time is defined as "T s ” and the time to actual centrifugation is “T i ” and the signal strength estimate at the standard time is called “SI s ” and actual signal intensity as “SI i ", the estimated signal intensity at the standard time can be calculated using the following formula.

[0037] SI s =SI i -a 0 ×(T i-T s ) That is, this formula is an estimation model, and the estimated value of the signal intensity at the standard time obtained thereby becomes the corrected signal intensity.

[0038] In the above description, a method of correcting one preparation condition has been described, but the correction is not limited to one type of preparation condition, and may be performed for multiple types of preparation conditions. When performing correction for multiple types of preparation conditions, correction may be performed stepwise by performing correction for the first preparation condition, and then performing correction based on the second preparation condition on the corrected marker data. Alternatively, correction may be performed only once using a regression equation obtained by performing multiple regression analysis.

[0039] In addition, when testing is performed using multiple disease markers (e.g., multiple types of miRNA), the fluctuation of marker data due to preparation conditions differs for each disease marker. Therefore, the above-mentioned correction is performed for each disease marker, and discrimination is performed using the corrected marker data obtained for each disease marker. Note that testing for a disease using multiple disease markers, i.e., determining whether or not a patient is afflicted, may be performed according to a conventionally known method.

[0040] (Determining whether or not someone is ill) In the test method according to the present embodiment, the presence or absence of disease is determined using the corrected signal intensity obtained as described above. Specifically, if the corrected signal intensity exceeds a pre-specified threshold, the sample to be tested is determined to be positive (has disease). On the other hand, if it is equal to or less than the threshold, the sample is determined to be negative (does not have disease). A plurality of pre-specified thresholds may be set in stages. In this case, for example, the degree of disease risk of the sample to be tested is determined in a plurality of stages depending on which of the plurality of thresholds the corrected signal intensity exceeds. For example, a first threshold and a second threshold lower than the first threshold may be set as thresholds, and if it exceeds the first threshold, it is determined to be high risk, if it is equal to or less than the first threshold and equal to or more than the second threshold, it is determined to be medium risk, and if it is equal to or less than the second threshold, it is determined to be low risk.

[0041] [Effects of this embodiment] As described above, in the present embodiment, in the disease testing method using marker data, even if there is variation in the specific preparation conditions for each sample, marker data under predetermined preparation conditions is estimated, and the presence or absence of a disease is determined based on the estimated value. As a result, the accuracy of the determination result indicating the presence or absence of a disease can be improved even for a body fluid sample prepared under conditions other than the originally intended preparation conditions. In particular, when there is a large difference between the originally intended preparation conditions and the actual preparation conditions, a greater improvement in accuracy is expected.

[0042] The acquisition of preparation data does not impose additional burdens, such as strict adherence to detailed protocols, on the medical site that actually samples the specimen. In other words, it is sufficient to simply record what protocol or conditions were used for sampling, while allowing for a range of possible specimen collection conditions, and provide the information to the user who actually performs the test. In addition, this information can be easily acquired or confirmed even after the marker data has been acquired. Therefore, the burden on the medical site is extremely small when the test method according to this embodiment is implemented. In addition, even if disease markers that can fluctuate during a relatively short operation time are included in the analysis target, highly accurate discrimination results can be obtained without excluding them.

[0043] In particular, when a test is performed using a plurality of disease markers, the state of the disease marker fluctuation that may occur due to the difference in parameters corresponding to the preparation data may differ for each disease marker. Therefore, when using a plurality of disease markers to improve the test accuracy, it is necessary to reduce the disease marker fluctuation, and more strict compliance with the protocol is required. However, according to this embodiment, since it is only necessary to record information, it can be more suitably applied to the test using a plurality of disease markers.

[0044] [Configuration of the inspection device] Next, an inspection device according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a functional block diagram showing a schematic configuration of an inspection device and a terminal device according to this embodiment. As shown in Fig. 1, the inspection device 100 is configured to communicate with a terminal device 200 equipped with a display unit, an input device, and the like. However, the inspection device 100 is not limited to the configuration of this embodiment, and for example, the inspection device 100 may be equipped with a display unit, an input device, and the like by itself without communicating with the terminal device 200.

[0045] The inspection device 100 includes a control unit 110, a storage unit 120, and a communication unit 130. The control unit 110 includes an acquisition unit (data acquisition unit) 140, a correction unit 150, and a discrimination unit 160. The acquisition unit 140 includes a marker data acquisition unit 141 and a preparation data acquisition unit 142.

[0046] The control unit 110 controls the inspection device 100 in an overall manner.

[0047] The storage unit 120 is a storage device that stores data necessary for processing by the inspection device 100. The storage unit 120 also stores an estimation model 121. The storage unit 120 may be an external device of the inspection device 100. For example, the storage unit 120 may be a storage device such as a server that is communicably connected to the inspection device 100.

[0048] The marker data acquisition unit 141 acquires marker data indicating the results of measuring disease markers in a body fluid sample. The preparation data acquisition unit 142 acquires preparation data indicating the preparation conditions of the body fluid sample. The acquisition unit 140 stores the marker data and the preparation data for the same body fluid sample in the memory unit 120 in association with each other.

[0049] The correction unit 150 corrects the marker data and calculates an estimated value of the marker data under predetermined preparation conditions using the estimation model 121 stored in the storage unit 120. The calculated value is output as corrected marker data.

[0050] The discrimination unit 160 uses the corrected marker data created by the correction unit 150 to discriminate whether or not the subject on which the input marker data is based is affected by the target disease. Specifically, if the value indicated by the corrected marker data exceeds a pre-specified threshold, the discrimination unit 160 discriminates that the specimen to be tested is positive (affected). On the other hand, if the value is equal to or less than the threshold, the discrimination unit 160 discriminates that the specimen is negative (not affected). Then, the discrimination unit 160 transmits the result to the terminal device 200. A plurality of pre-specified thresholds may be provided in stages. Also, the testing device 100 may not be configured to include the discrimination unit 160. In this case, the corrected marker data created by the correction unit 150 may be directly transmitted to the terminal device 200, and the user may discriminate whether the specimen is positive / negative based on a predetermined criterion.

[0051] In the present embodiment, the inspection device 100 is described in which the control unit 110 is provided with the correction unit 150. However, the inspection device 100 does not necessarily have to be provided with the correction unit 150. That is, the above-mentioned estimation model 121 may be constructed by a device that exists independently of the inspection device 100 and includes at least the acquisition unit 140 including the marker data acquisition unit 141 and the preparation data acquisition unit 142, and the correction unit 150. When the device exists independently of the inspection device 100, the inspection device 100 can use the estimation model 121 by reading the estimation model 121 stored in a storage medium. Alternatively, the inspection device 100 can use the estimation model 121 by receiving the estimation model 121 from another device via a wired or wireless network.

[0052] The terminal device 200 includes a communication unit 210, a control unit 220, an input device 230, and a display unit 240. The communication unit 210 is a communication interface that transmits and receives data to and from the inspection device 100 via wired or wireless communication. The control unit 220 performs overall control of the terminal device 200. The display unit 240 is a display that can display images, characters, and the like. The input device 230 accepts user input, and is realized by, for example, a touch panel, a mouse, a keyboard, and the like. When the input device 230 is a touch panel, the touch panel is provided on the display unit 240. A user can use the functions of the inspection device 100 via the terminal device 200.

[0053] The inspection apparatus 100 is an apparatus suitable for carrying out the inspection method according to the present embodiment described above.

[0054] [Software implementation example] The control block of the inspection device 100 (the control unit 110, in particular the acquisition unit 140, the correction unit 150 and the discrimination unit 160) may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software.

[0055] In the latter case, the inspection device 100 includes a computer that executes instructions of a program, which is software that realizes each function. The computer includes, for example, at least one processor (control device) and at least one computer-readable recording medium that stores the program. The object of the present invention is achieved by having the processor read the program from the recording medium and execute it in the computer. The processor may be, for example, a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The recording medium may be a "non-transient tangible medium", such as a ROM (Read Only Memory), a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like. The device may further include a RAM (Random Access Memory) that expands the program. The program may be supplied to the computer via any transmission medium (such as a communication network or a broadcast wave) that can transmit the program. Note that one aspect of the present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

[0056] 〔summary〕 The testing method of the present invention is a testing method for testing for a disease using a disease marker, and includes a data acquisition step of acquiring marker data indicating the measurement results of measuring a disease marker in a body fluid sample collected from a subject, and preparation data indicating the preparation conditions of the body fluid sample, a correction step of correcting the acquired marker data using the acquired preparation data to obtain corrected marker data, and a discrimination step of discriminating the presence or absence of a disease in the subject based on the corrected marker data, where the corrected marker data is an estimate of the value of the marker data under specified preparation conditions of the same type as the indicator in the acquired preparation data.

[0057] In addition, in one aspect of the testing method of the present invention, the correction process corrects the marker data by utilizing a regression equation created by regression analysis using multiple preparation data of the same index and multiple marker data corresponding to each preparation data.

[0058] In addition, in one embodiment of the testing method of the present invention, the preparation data includes at least one piece of information selected from information on the subject, information indicating the collection conditions of the body fluid sample, and condition information on the treatment performed on the body fluid sample before measuring the disease marker.

[0059] In one embodiment of the testing method according to the present invention, the conditions of the treatment performed on the body fluid sample are at least one condition selected from the group consisting of the time until the body fluid sample is frozen and stored, the temperature during the frozen storage, the time for which the body fluid sample is frozen and stored, and the time until the body fluid sample is centrifuged.

[0060] In one aspect of the testing method of the present invention, the information indicating the collection conditions of the body fluid sample is at least one piece of information selected from the thickness of the needle used to collect the body fluid sample, the type of blood collection tube used to collect the body fluid sample, and the time between the subject's last meal and the collection of the body fluid sample.

[0061] In one aspect of the testing method according to the present invention, the preparation data includes information about the subject, the information being information about the blood cell mass of the subject.

[0062] In one aspect of the testing method according to the present invention, the information on the subject is information on the race of the subject.

[0063] In one aspect of the testing method according to the present invention, the body fluid sample is blood, serum, plasma, cerebrospinal fluid, urine, saliva, tears, tissue fluid, or lymphatic fluid.

[0064] In a further aspect of the testing method according to the present invention, the body fluid sample is blood, serum or plasma.

[0065] In one embodiment of the testing method according to the present invention, the disease marker is miRNA.

[0066] In one aspect of the testing method according to the present invention, the marker data is data obtained from a microarray, PCR, or sequencing.

[0067] The testing device of the present invention is a testing device that performs disease testing using disease markers, and is equipped with a data acquisition unit that acquires marker data indicating the results of measuring disease markers in a body fluid sample collected from a subject, and preparation data indicating the preparation conditions of the body fluid sample, and a correction unit that uses the acquired preparation data to correct the acquired marker data to acquire corrected marker data, where the corrected marker data is an estimate of the value of the marker data under specified preparation conditions of the same type as the indicator in the acquired preparation data.

[0068] In addition, one aspect of the testing apparatus according to the present invention further includes a discrimination unit that discriminates the presence or absence of a disease in the subject based on the corrected marker data.

[0069] The inspection device according to each aspect of the present invention may be realized by a computer. In this case, an inspection program that causes a computer to operate as each part (software element) of the inspection device to realize the inspection device, and a computer-readable recording medium on which the inspection program is recorded, also fall within the scope of the present invention.

[0070] The following examples are provided to further explain the embodiments of the present invention. Of course, the present invention is not limited to the following examples, and various modifications are possible in detail. Furthermore, the present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. The embodiments obtained by appropriately combining the technical means disclosed herein are also included in the technical scope of the present invention. EXAMPLES

[0071] [Reference Example 1] <Collection of Specimens> Informed consent was obtained from 35 patients with pancreatic cancer or biliary tract cancer diagnosed by histopathological examination and 49 healthy subjects, and their respective blood samples were collected. Serum was obtained by centrifugation. The obtained serum was transferred to a freezer at -80°C within 2 hours after blood collection and stored at -80°C until use. Note that there was a certain variation among specimens in the time required from blood collection to storage at -80°C.

[0072] <Extraction of total RNA> As specimens, 300 μL of each serum obtained from each of the above 84 people was used. Using the RNA extraction reagent in the 3D-Gene (registered trademark) RNA extraction reagent from liquid sample kit (Toray Industries, Inc. (Japan)), total RNA was obtained from each serum according to the protocol defined by the company. Hereinafter, the specimen group in Reference Example 1 is referred to as Specimen Group 1.

[0073] <Measurement of Gene Expression Level> For the total RNA obtained from the sera of each of the above 84 people, miRNAs in the total RNA were fluorescently labeled using the 3D-Gene (registered trademark) miRNA Labeling kit (Toray Industries, Inc.) based on the protocol defined by the company. As an oligo DNA chip, a 3D-Gene (registered trademark) Human miRNA Oligo chip (Toray Industries, Inc.) equipped with probes having sequences complementary to miRNAs registered in miRBase release 21 was used, and hybridization and washing after hybridization were performed under stringent conditions based on the protocol defined by the company. The DNA chip was scanned using a 3D-Gene (registered trademark) scanner (Toray Industries, Inc.), an image was obtained, and the fluorescence intensity was quantified using 3D-Gene (registered trademark) Extraction (Toray Industries, Inc.).

[0074] The expression levels of the genes detected as follows were calculated using the digitized fluorescence intensities. First, excluding the top 5% and bottom 5% of the signal intensities of multiple negative control spots, the [average value + 2 × standard deviation] was calculated, and genes showing signal intensities greater than this value were considered detected. Also, from the signal intensities of the detected genes, the average value of the signal intensities of the negative control spots excluding the top 5% and bottom 5% was subtracted, and the subtracted value was converted to the logarithm to the base 2 and used as the gene expression level.

[0075] Calculations and statistical analyses using the digitized gene expression levels of miRNAs were performed using "Excel Statistics (Bellcurve (registered trademark) for Excel)" (Social Information Service Co., Ltd.).

[0076] [Reference Example 2] [Sample collection] Informed consent was obtained from 18 patients with pancreatic cancer or biliary tract cancer and 23 healthy subjects, and blood samples were collected from each of them, and serum was obtained by centrifugation. The obtained serum was transferred to a freezer at -80°C within 6 hours after blood collection and stored at -80°C until use. Note that there was a certain variation among the samples in the time required from blood collection to storage at -80°C.

[0077] [Extraction of total RNA] Using 300 μL of each serum obtained from the above 41 individuals as samples, total RNA was obtained in the same manner as in Reference Example 1. Hereinafter, the sample group in Reference Example 2 is referred to as sample group 2.

[0078] [Measurement of gene expression level] Using the total RNA obtained from the sera of the above 41 individuals, the gene expression levels of miRNAs were obtained in the same manner as in Reference Example 1.

[0079] [Reference Example 3] [Sample collection] Blood was collected after obtaining informed consent from healthy individuals, and serum was obtained by centrifugation. The obtained serum was sampled at four time points in an arithmetic progression as the time from blood collection to storage at -80°C, and stored at -80°C until use.

[0080] <Extraction of total RNA> As samples, 300 μL of each serum obtained above was used, and total RNA was obtained in the same manner as in Reference Example 1. Hereinafter, the sample group in Reference Example 3 is referred to as Sample Group 3.

[0081] <Measurement of gene expression level> Using the total RNA obtained from each of the above sera, the gene expression level of miRNA was obtained in the same manner as in Reference Example 1.

[0082] [Example 1] <Regression analysis of the time from blood collection to cryopreservation and the gene expression level of miRNA> In this example, linear regression analysis was performed as an example of regression analysis. Linear regression analysis was performed using the time required from blood collection to storage at -80°C of the serum obtained in Reference Example 3 and the gene expression level of miRNA, and the change coefficient of the gene expression level of miRNA per unit time was obtained. In the regression equation obtained by this analysis, y is the gene expression level of miRNA, and x is the time (h) required from blood collection to storage at -80°C.

[0083] In this example, miR-4687-3p was arbitrarily selected as the miRNA to be used, and the regression equation y = -0.5050x + 11.55 was obtained. That is, the change coefficient of the gene expression level of miR-4687-3p was -0.5050, indicating that the gene expression level decreased by 0.5050 per unit time.

[0084] [Example 2] <Verification 1 of the discriminative performance for pancreatic cancer and biliary tract cancer> In this example, the change coefficient of the gene expression level of miR-4687-3p obtained in Example 1 was used for correction, and the estimated gene expression level was obtained when the time required from blood collection to storage at -80°C for sample group 1 was 0.5 hours, and the discrimination performance between cancer patients and healthy subjects was confirmed. Specifically, an ROC curve was created from the calculated estimated gene expression level, and the discrimination performance was confirmed based on the area under the ROC curve (AUC).

[0085] In order to distinguish between pancreatic cancer and biliary tract cancer, the following stepwise procedure was taken. That is, for each specimen of specimen group 1, the gene expression level of miR-4687-3p and the time required from blood collection to storage at -80°C, and the change coefficient (-0.5050) obtained in Example 1 were used to obtain the estimated gene expression level of each specimen when the time required from blood collection to storage at -80°C is 0.5 hours. For example, when the gene expression level of miR-4687-3p is 12.8 and the time required from blood collection to storage at -80°C is 0.85 hours, the change in the gene expression level of miR-4687-3p when the time required from blood collection to storage at -80°C is 0.5 hours is -0.5050*(0.85-0.5)=-0.1768. Therefore, the estimated gene expression level of miR-4687-3p when the time required from blood collection to storage at -80°C is 0.5 hours can be calculated as 12.8 - (-0.1768) = 12.98.

[0086] In this way, the estimated gene expression level was calculated for each sample in sample group 1 when the time required from blood collection to storage at -80°C was 0.5 hours. An ROC curve was created based on the calculated estimated gene expression level and the information on the presence or absence of disease, and the area under the ROC curve (AUC) was calculated. As a result, the AUC was 0.9041. The created ROC curve is shown in Figure 2. In Figure 2 and Figure 3 described later, the "true positive rate" represents the proportion of cases correctly judged as positive by the test, that is, the sensitivity. In addition, the "false positive rate" is the proportion of cases erroneously judged as positive by the test, and is calculated as 1-(specificity). Note that specificity refers to the proportion of cases correctly judged as negative by the test.

[0087] [Example 3] <Verification of pancreatic and biliary tract cancer discrimination performance 2> In this example, correction was performed using the change coefficient of the gene expression level of miR-4687-3p obtained in Example 1, and the estimated gene expression level was calculated when the time required from blood collection to storage at -80°C for sample group 2 was 0.5 hours.The discrimination performance between cancer patients and healthy individuals was confirmed in the same manner as in Example 2.

[0088] The following stepwise procedure was taken to distinguish between pancreatic cancer and biliary tract cancer. That is, for each sample in sample group 2, the gene expression level of miR-4687-3p, the time required from blood collection to storage at -80°C, and the coefficient of change (-0.5050) obtained in Example 1 were used to calculate the gene expression level of each sample when the time required from blood collection to storage at -80°C was 0.5 hours, in the same manner as in Example 2. An ROC curve was created based on the calculated estimated gene expression level and the information on the presence or absence of disease, and the AUC was calculated. As a result, the AUC was 0.8720. The created ROC curve is shown in FIG. 3.

[0089] [Comparative Example 1] <Verification of pancreatic and biliary tract cancer discrimination performance 1> In this comparative example, an ROC curve was created based on the gene expression levels of miRNA in sample group 1 and the presence or absence of disease, and the AUC was calculated to confirm the discrimination performance between cancer patients and healthy subjects.

[0090] A ROC curve was created using the gene expression levels of each miR-4687-3p in sample group 1, and the AUC was calculated, resulting in a value of 0.9117. The created ROC curve is shown in Figure 2. The difference from the AUC obtained in Example 2 was 0.0076, and when a chi-square test was performed with a significance level of 0.05, the result was p=0.0743, indicating no significant difference.

[0091] [Comparative Example 2] <Verification of pancreatic and biliary tract cancer discrimination performance 2> In this comparative example, an ROC curve was created based on the gene expression levels of miRNA in sample group 2 and the presence or absence of disease, and the AUC was calculated to confirm the discrimination performance between cancer patients and healthy subjects.

[0092] Using the gene expression levels of each miR-4687-3p in the sample group 2, an ROC curve was created and the AUC was determined. As a result, the AUC was 0.5604. The created ROC curve is shown in Figure 3. Also, the difference from the AUC obtained in Example 3 was 0.3116. When a chi-square test with a significance level of 0.05 was performed, p = 0.0186, indicating a significant difference. That is, it was shown that Example 3 using the estimated gene expression level was superior in discrimination performance to Comparative Example 2 that did not perform expression level correction.

[0093] [Reference Example 4] [Sampling of Specimens] Informed consent was obtained from 137 healthy individuals, and their respective blood samples were collected. Serum was obtained by centrifugation. Separately from this, blood was collected to obtain information on the white blood cell count.

[0094] [Extraction of total RNA] Using 300 μL of each serum obtained from the above 137 individuals as specimens, total RNA was obtained in the same manner as in Reference Example 1. Hereinafter, the specimen group in Reference Example 4 is referred to as specimen group 4.

[0095] [Measurement of Gene Expression Level] Using the total RNA obtained from the sera of the above 137 individuals, the gene expression levels of miRNAs were obtained in the same manner as in Reference Example 1.

[0096] [Example 4] [Regression Analysis of White Blood Cell Count and miRNA Gene Expression Level] In this example, linear regression analysis was performed as an example of regression analysis. Linear regression analysis was performed using the white blood cell count (logarithm to the base 2) and the miRNA gene expression level of the serum obtained in Reference Example 4, and the coefficient of change in the miRNA gene expression level per unit time was obtained. In the regression equation obtained by this analysis, y is the miRNA gene expression level and x is the white blood cell count.

[0097] In this example, miR-6778-5p was arbitrarily selected as the miRNA to be used, and the regression equation y = 0.8678x - 1.4445 was obtained. That is, the coefficient of variation of the gene expression level of miR-6778-5p was 0.8678, indicating that the gene expression level increased by 0.8678 per unit white blood cell count. There has been no report so far that the white blood cell count is related to the presence or absence of pancreatic cancer or biliary tract cancer.

[0098] [Reference Example 5] <Sample Collection> Informed consent was obtained from 3 patients with pancreatic cancer or biliary tract cancer and 3 healthy subjects, and their respective blood samples were collected. Serum was obtained by centrifugation. Additionally, blood was collected separately to obtain information on the white blood cell count.

[0099] <Extraction of total RNA> Using 300 μL of each serum obtained from each of the above 6 individuals as samples, total RNA was obtained in the same manner as in Reference Example 1. Hereinafter, the sample group in Reference Example 5 is referred to as Sample Group 5.

[0100] [Example 5] <Verification of Discrimination Performance for Pancreatic Cancer and Biliary Tract Cancer 3> In this example, correction was performed using the coefficient of variation of the gene expression level of miR-6778-5p obtained in Example 4. When the white blood cell count (logarithm to the base 2) of Sample Group 5 was 12.4, the estimated gene expression level was determined, and the discrimination performance between cancer patients and healthy subjects was confirmed. Specifically, for the calculated estimated gene expression levels, the average values of 3 cancer patients and the average values of 3 healthy subjects were determined and tested by Welch's t-test.

[0101] The following stepwise procedure was taken to distinguish between pancreatic cancer and biliary tract cancer. That is, for each sample in sample group 5, the gene expression level of miR-6778-5p and the white blood cell count, and the coefficient of change (0.8678) obtained in Example 4 were used to obtain the estimated gene expression level of each sample when the white blood cell count (logarithm of base 2) was 12.4. For example, when the gene expression level of miR-6778-5p is 9.9 and the white blood cell count is 12.7, the change in the gene expression level of miR-4687-3p when the white blood cell count is 12.4 is 0.8678*(12.7-12.4)=0.2603. Therefore, the estimated gene expression level of miR-6778-5p when the white blood cell count is 12.4 can be calculated as 9.9-(0.2603)=9.640.

[0102] In this way, the estimated gene expression level was calculated for each sample in sample group 5 when the white blood cell count (logarithm of base 2) was set to 12.4. Next, based on the calculated estimated gene expression level and the information on the presence or absence of disease, the average value of the estimated gene expression level in the group of cancer patients and the average value of the estimated gene expression level in the group of healthy subjects were obtained, and the difference was calculated. As a result, the difference in the average value of the estimated gene expression level was 0.71. In addition, Welch's t-test showed p<0.01, indicating a significant difference. On the other hand, the difference in the average value of the gene expression level in each group before correction was 0.47. In addition, there was no significant difference in Welch's t-test. The respective graphs (box plots) comparing the average values ​​are shown in Figure 4. In Figure 4, the left side is the graph before correction, and the right side is the graph after correction. [Industrial Applicability]

[0103] The present invention can be used in disease testing using disease markers. [Explanation of symbols]

[0104] 100 Inspection Equipment 110 Control section 120 Storage section 121 Estimation Model 130 Communications Department 140 Acquisition Department 141 Marker Data Acquisition Unit 142 Preparation Data Acquisition Unit 150 Correction section 160 Discrimination part

Claims

1. A method for testing for diseases including pancreatic cancer and biliary tract cancer using a test device with a disease marker that is DNA, RNA, or protein, comprising: a data acquisition step of acquiring marker data indicating the measurement results of the disease markers in a body fluid sample, which is blood, serum or plasma, collected from a subject, and preparation data indicating the preparation conditions of the body fluid sample; a correction step of correcting the acquired marker data using the acquired preparation data to acquire corrected marker data; and determining, by a discrimination unit of the testing device, whether or not the subject is afflicted with a disease based on the corrected marker data, The corrected marker data is an estimate of a value of the marker data under a predetermined preparation condition of the same type as an index in the acquired preparation data, In the correction step, the marker data is corrected using a regression equation created by regression analysis using a plurality of the preparation data of the same index and a plurality of the marker data corresponding to each preparation data, A testing method, wherein the preparation data includes information about the subject, the information including information about the blood cell volume of the subject.

2. The testing method of claim 1 , wherein the preparation data includes at least one piece of information selected from information on the subject, information indicating collection conditions for the bodily fluid sample, and condition information on processing performed on the bodily fluid sample before measuring the disease marker.

3. 3. The testing method according to claim 2, wherein the conditions of the treatment applied to the body fluid sample are at least one condition selected from the group consisting of the time until the body fluid sample is frozen and stored, the temperature during the frozen storage, the time for which the body fluid sample is frozen and stored, and the time until the body fluid sample is centrifuged.

4. 3. The testing method of claim 2, wherein the information indicating the collection conditions of the body fluid sample is at least one piece of information selected from the thickness of the needle used to collect the body fluid sample, the type of blood collection tube used to collect the body fluid sample, and the time between the subject's last meal and the collection of the body fluid sample.

5. The testing method according to claim 2 , wherein the information on the subject is information on the race of the subject.

6. The method according to any one of claims 1 to 5, wherein the disease marker is miRNA.

7. The testing method according to claim 6 , wherein the marker data is data obtained from a microarray, PCR or sequencing.

8. A testing device for testing for diseases including pancreatic cancer and biliary tract cancer using a disease marker that is DNA, RNA, or protein, comprising: a data acquisition unit that acquires marker data indicating the results of measuring the disease markers in a body fluid sample, which is blood, serum, or plasma, collected from a subject, and preparation data indicating the preparation conditions of the body fluid sample; a correction unit that corrects the acquired marker data using the acquired preparation data to acquire corrected marker data; The corrected marker data is an estimate of a value of the marker data under a predetermined preparation condition of the same type as an index in the acquired preparation data, The correction unit corrects the marker data by using a regression equation created by regression analysis using a plurality of the preparation data of the same index and a plurality of the marker data corresponding to each preparation data, An inspection apparatus, wherein the preparation data includes information about the subject, the information including information about the blood cell volume of the subject.

9. The examination apparatus according to claim 8 , further comprising a discrimination unit that discriminates the presence or absence of a disease in the subject based on the corrected marker data.

10. 9. An inspection program for causing a computer to function as the inspection apparatus according to claim 8, the inspection program causing a computer to function as the data acquisition unit and the correction unit.

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