WO2021153753A1 - Procédé, dispositif et programme d'examen - Google Patents

Procédé, dispositif et programme d'examen Download PDF

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WO2021153753A1
WO2021153753A1 PCT/JP2021/003311 JP2021003311W WO2021153753A1 WO 2021153753 A1 WO2021153753 A1 WO 2021153753A1 JP 2021003311 W JP2021003311 W JP 2021003311W WO 2021153753 A1 WO2021153753 A1 WO 2021153753A1
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data
body fluid
fluid sample
disease
marker
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PCT/JP2021/003311
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English (en)
Japanese (ja)
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真紀子 吉本
淳 渥美
敦子 宮野
千絵 岩▲崎▼
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東レ株式会社
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Priority to JP2021507533A priority Critical patent/JPWO2021153753A1/ja
Publication of WO2021153753A1 publication Critical patent/WO2021153753A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/686—Polymerase chain reaction [PCR]
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

  • the present invention relates to a test method, a test device, and a test program for testing a disease using a disease marker.
  • Patent Document 1 discloses that after storing a sample in a serum state at 4 ° C. for 72 hours or 168 hours, the abundance of a part of miRNA in the sample fluctuates significantly. Therefore, it is common practice to unify the protocol, such as by aligning the test conditions including the collection of samples.
  • the present invention has been made in view of the above problems, and an object of the present invention is to allow a wide range of feasible sample collection conditions without imposing an excessive burden on the medical field, and to have high accuracy.
  • the purpose is to provide a method for testing a disease.
  • the test method according to the present invention is a test method for testing a disease using a disease marker in order to solve the above problems, and shows a measurement result obtained by measuring a disease marker in a body fluid sample collected from a subject.
  • the determination step of determining the presence or absence of disease in the subject based on the corrected marker data, the corrected marker data is a predetermined preparation condition of the same type as the index in the acquired preparation data. The value of the above marker data in the above is estimated.
  • the testing device is a testing device that tests for a disease using a disease marker in order to solve the above problems, and is a marker showing the result of measuring a disease marker in a body fluid sample collected from a subject.
  • a data acquisition unit that acquires data and preparation data indicating the preparation conditions of the body fluid sample, and a correction unit that corrects the acquired marker data using the acquired preparation data and acquires the corrected marker data.
  • the corrected marker data is an estimate of the value of the marker data under predetermined preparation conditions of the same type as the index in the acquired preparation data.
  • the medical field is not overloaded. It is possible to carry out highly accurate disease inspections.
  • the test method in the present embodiment is a method for testing a disease using a disease marker, and indicates the result of measuring the disease marker in the body fluid sample using the preparation data showing the preparation conditions of the body fluid sample. This is a test method for determining the presence or absence of a disease after correcting the above.
  • body fluid sample refers to a body fluid collected from a subject used for an examination.
  • the body fluid is not particularly limited as long as it can be used for a test for measuring a disease marker, and examples thereof include blood, serum, plasma, cerebrospinal fluid, urine, saliva, tears, tissue fluid, and lymph fluid. Among these, blood, serum and plasma are preferably used.
  • Disease marker refers to a biomolecule whose presence or abundance is related to a specific disease. As used herein, “disease marker” refers to a disease marker in the disease being tested. Therefore, the term “disease marker” as used herein refers to a biomolecule whose presence or abundance is known to be related to the disease to be tested. Disease markers include, for example, DNA, RNA and proteins. Among these, RNA is preferably used, and non-coding RNA (ncRNA) is more preferably used. NcRNAs are roughly classified into small molecule ncRNAs having a length of about 20 to 200 bases and long-chain ncRNAs having a total length of several hundred bases to several hundred thousand bases.
  • ncRNA examples include translocated RNA, ribosome RNA, nuclear small RNA, nuclear body small RNA, signal recognition complex RNA, miRNA, piRNA, long non-coding RNA, circular RNA, and untranslated region of mRNA.
  • MiRNA is particularly preferably used.
  • 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 disease, and allergy.
  • cancer include pancreatic cancer, biliary tract cancer, breast cancer, lung cancer, colon cancer, esophageal cancer, gastric cancer, liver cancer, prostate cancer, bladder cancer, brain cancer, hematological cancer, ovarian cancer, uterine cancer and the like. It can be pancreatic cancer and biliary tract cancer.
  • the "marker data” is data indicating the presence or absence or abundance of one or more disease markers obtained by measuring the disease markers in the body fluid sample.
  • the "abundance amount” can be rephrased as the "expression level”.
  • “measuring” can be paraphrased as "detecting.”
  • the marker data in the present embodiment is not limited to the result obtained by measuring one specific disease marker, but a plurality of measurement results or numerical values corresponding to each disease marker obtained by measuring a plurality of disease markers. It may include data.
  • the measurement results of the plurality of disease markers may be the results of simultaneous measurement of each disease marker or the results of independent measurement.
  • 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 measuring means for obtaining the marker data can be appropriately selected depending on the biomolecule to be measured and the necessary data. Examples of the measuring means include microarrays, various PCRs including qRT-PCR, various sequencings including next-generation sequencing, ELISA and the like. Among them, it is preferable to use a microarray from the viewpoint that a plurality of disease markers can be measured at the same time and a highly accurate test can be performed by using the plurality of disease markers.
  • the conversion method is not particularly limited as long as there is a correlation between the measurement result and the numerical data.
  • the term "subject” refers to primates including humans and chimpanzees, pet animals such as dogs and cats, domestic animals such as cows, horses, sheep and goats, rodents such as mice and rats, and zoos. Means mammals such as animals bred in. A preferred subject is human.
  • a "healthy person” is an individual of the same species as the target subject and is not affected by the target disease.
  • the "preparation data” is data indicating the preparation conditions of the body fluid sample, and includes one or more information regarding the body fluid sample other than the measurement result of the disease marker obtained by the measurement. Specifically, as an example of the information contained in the preparation data, the information on the subject from which the body fluid sample was collected, the information indicating the collection conditions of the body fluid sample, and the information applied to the body fluid sample before the measurement of the disease marker in the body fluid sample are applied. Information on the processing conditions can be mentioned. If the subject or the condition of the subject is different, or if the collection conditions or processing conditions of the body fluid sample are different, the profile of the disease marker in the body fluid sample may fluctuate to some extent.
  • Information on the subject is information about the subject itself or information about the physical condition of the subject at the time of collecting the body fluid, which is not directly related to the collection of the body fluid sample.
  • Such information includes, for example, the race of the subject, biomolecular information that can be obtained from the blood of the subject and is not related to the disease to be tested, the dietary content and feeding time on the day or the day before the sample collection, and the subject.
  • Information on the physical condition such as the time from the final eating and drinking of the sample to the collection of body fluid, and the time from taking the drug type or drug to the collection when the subject was taking the drug. Can be mentioned.
  • biomolecules such as blood cells, proteins, lipids, and sugars.
  • information on biomolecules whose correlation with the disease to be tested is unknown may be used.
  • the biomolecular information that can be obtained from the blood of the subject, whose correlation with the presence or absence of these diseases is unknown is the amount of blood cells (erythrocyte amount, red blood cell amount, etc.).
  • the amount of white blood cells or platelets), the amount of proteins such as PSA and CYFRA, and the test values such as the amount of HDL cholesterol and the amount of LDL cholesterol can be mentioned.
  • Information indicating the conditions for collecting body fluid samples is information on the conditions for collecting body fluids and the equipment used. Such information includes the type of blood collection needle used when a needle was used to collect body fluid, and the blood collection tube used to collect blood when the body fluid was blood or a component derived from blood. Examples include the type, the type of coagulation promoter or coagulation inhibitor added to the blood collection tube, and the type of blood vessel (capillary blood or venous blood) at the time of blood collection.
  • the “conditions for the treatment applied to the body fluid sample” are the conditions for various treatments and operations applied to the body fluid sample before the measurement of the disease marker, and include the conditions for the storage of the body fluid sample.
  • Preservation conditions include, for example, the time and temperature until freezing or transfer to a freezer, and the temperature at the time of cryopreservation, when the body fluid is collected and then cryopreserved without immediately analyzing the disease marker.
  • Time to cryopreserve time to inactivate degrading enzymes in body fluid specimens, such as RNase, DNase or Protease, material of specimen storage container during cryopreservation, and temperature at the start of cryopreservation. Whether or not packing materials are used to soften the impact of changes can be mentioned.
  • the conditions for various treatments and operations that can be applied to the body fluid sample include, when the body fluid is centrifuged, or when the collected blood is centrifuged to obtain serum which is an actual body fluid sample.
  • the time from collecting the body fluid from the subject to performing the centrifugation operation, the centrifugal acceleration, and the like can be mentioned.
  • an actual body fluid sample can be obtained by performing a centrifugation operation on the collected body fluid
  • the time until the body fluid sample is frozen or transferred to the freezer after the centrifugation operation is also the time of the treatment applied to the body fluid sample. It is given as an example of the condition.
  • the time from the frozen state to the thawing of the body fluid sample, the temperature for thawing, and the like can be mentioned.
  • preparation conditions are conditions other than numerical values, such as race, type of blood collection tube, and type of drug to be taken, different numerical values are given to each of the assumed specific candidates, and the regression equation is performed using the numerical values. Should be created.
  • the preparation data may include at least one of the above information, and may include 2 or more, 3 or more, or 4 or more.
  • the test method includes a step of acquiring marker data indicating the measurement result 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, and the acquired preparation. It includes a step of correcting the acquired marker data by using the data to acquire the corrected marker data, and a step of determining whether or not the subject has a disease based on the corrected marker data.
  • the corrected marker data is an estimate of the value of the marker data under a predetermined preparation condition of the same type as the index in the acquired preparation data.
  • the "predetermined preparation condition of the same type as the index in the preparation data” is the same type of preparation condition as the preparation condition used in the acquired preparation data, and is a specific condition arbitrarily set in advance. means. For example, when the preparation data using "the time from collecting the body fluid from the subject to performing the centrifugation operation" as the preparation condition is acquired, “the body fluid is collected from the subject and then centrifuged” which is arbitrarily set in advance. It means “time until the separation operation is performed", for example, 0.5 hour.
  • the marker data correction method is not particularly limited as long as the value of the marker data under the same type of predetermined preparation conditions as the index in the acquired preparation data can be estimated.
  • a regression equation is prepared in advance by regression analysis using a plurality of preparation data of the same index and a plurality of marker data corresponding to each preparation data, and the regression equation is used.
  • the use of the regression equation is not limited to the case of using the obtained regression equation itself, but also the case of using a numerical value such as a coefficient obtained by creating the regression equation.
  • the regression analysis is typically a linear regression analysis, which may be either a simple regression analysis or a multiple regression analysis, preferably a simple regression analysis.
  • the method of creating a regression equation in this embodiment includes the following steps: (A) Obtain the signal intensity of miRNA using a plurality of body fluid samples derived from healthy subjects with various time to centrifugation.
  • step (a) in order to suppress fluctuations in marker data due to other factors, it is desirable that the conditions other than the time until centrifugation are the same for each body fluid sample as much as possible. Therefore, in the above example, a plurality of body fluid samples differing only in the time until centrifugation are prepared from the same body fluid before centrifugation, and the conditions after preparation (for example, the time until storage at -80 ° C) are the same. It is preferable to prepare a plurality of body fluid samples.
  • the signal intensity is corrected by substituting a predetermined standard time into x of the regression equation peculiar to each obtained body fluid sample. That is, the numerical value obtained from the regression equation by substituting the standard time for x of the unique regression equation becomes the estimated value of the signal intensity in the standard time, that is, the corrected signal intensity.
  • the marker data is corrected by using an estimation model using only a part of the coefficients of the regression equation obtained in the above-mentioned specific aspect 1.
  • SI s SI i -a 0 ⁇ (T i -T s) That is, the equation is an estimation model, and the estimated value of the signal intensity in the standard time obtained by this is the corrected signal intensity.
  • the method of making corrections for one type of preparation condition has been described, but the correction is not limited to the correction of one type of preparation condition, and may be used for making corrections for a plurality of types of preparation conditions.
  • corrections are made step by step by making corrections for the first preparation condition and then making corrections based on the second preparation condition for the corrected marker data. Just do it.
  • the regression equation obtained by performing the multiple regression analysis may be used to perform the correction only once.
  • the state of fluctuation of the marker data depending on the preparation conditions differs for each disease marker. Therefore, the above-mentioned correction may be performed for each disease marker, and the correction may be performed using the corrected marker data obtained for each.
  • the examination of the disease using a plurality of disease markers that is, the determination of the presence or absence of morbidity may be performed according to a conventionally known method.
  • the presence or absence of morbidity is determined by using the corrected signal intensity obtained as described above. Specifically, if the corrected signal intensity exceeds a threshold value specified in advance, it is determined that the sample to be tested is positive (affected). On the other hand, if it is equal to or less than the threshold value, it is determined that the sample is negative (not affected).
  • a plurality of predetermined threshold values may be provided stepwise. In this case, for example, the degree of morbidity 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.
  • a first threshold value and a second threshold value lower than the first threshold value are set as threshold values, and if the threshold value exceeds the first threshold value, it is determined that the risk is high, and the threshold value is equal to or less than the first threshold value and is the first. If it exceeds the threshold value of 2, it may be determined that the risk is in progress, and if it is equal to or less than the second threshold value, it may be determined that the risk is low.
  • the marker data under the predetermined preparation conditions is estimated even if there are variations for each sample with respect to the specific preparation conditions. Based on the estimated values, the presence or absence of disease is determined. As a result, it is possible to improve the accuracy of the discrimination result indicating the presence or absence of disease even in the body fluid sample prepared under the conditions deviating from the originally intended preparation conditions. In particular, when the difference between the originally intended preparation conditions and the actual preparation conditions is large, a greater improvement in accuracy is expected.
  • the change in the disease markers that may occur due to the difference in the parameters corresponding to the prepared data may differ for each disease marker. Therefore, when the test accuracy is improved by using a plurality of disease markers, it is necessary to reduce the fluctuation of the disease markers, and stricter adherence to the protocol is required.
  • it since it is only necessary to record the information, it can be more preferably applied to the examination using a plurality of disease markers.
  • FIG. 1 is a functional block diagram showing a schematic configuration of an inspection device and a terminal device according to the present embodiment.
  • the inspection device 100 is configured to communicate with a terminal device 200 including a display unit, an input device, and the like.
  • the inspection device 100 is not limited to the configuration of the present embodiment, and for example, the inspection device 100 may include a display unit, an input device, and the like by itself without communicating with the terminal device 200.
  • 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. Further, the acquisition unit 140 includes a marker data acquisition unit 141 and a preparation data acquisition unit 142.
  • the control unit 110 comprehensively controls the inspection device 100.
  • the storage unit 120 is a storage device that stores data necessary for processing of the inspection device 100. Further, the storage unit 120 stores the estimation model 121.
  • the storage unit 120 may be an external device of the inspection device 100.
  • the storage unit 120 may be a storage device such as a server that is communicably connected to the inspection device 100.
  • the marker data acquisition unit 141 acquires marker data indicating the result of measuring the disease marker in the 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 storage unit 120 in association with each other.
  • the correction unit 150 corrects the marker data using the estimation model 121 stored in the storage unit 120, and calculates an estimated value of the marker data under a predetermined preparation condition. The calculated value is output as corrected marker data.
  • the discrimination unit 160 uses the corrected marker data created by the correction unit 150 to determine whether or not the subject on which the input marker data is based suffers from the target disease. Specifically, if the numerical value indicated by the corrected marker data exceeds a threshold value specified in advance, the discriminating unit 160 determines that the sample to be tested is positive (affected). On the other hand, if it is equal to or less than the threshold value, it is determined that the sample is negative (not affected). Then, the discriminating unit 160 transmits the result to the terminal device 200.
  • a plurality of predetermined threshold values may be provided stepwise. Further, the inspection device 100 does not have to be provided with the discrimination unit 160. In this case, the corrected marker data created by the correction unit 150 may be transmitted to the terminal device 200 as it is, and the user may determine whether the sample is positive or negative based on a predetermined criterion.
  • the inspection device 100 having the correction unit 150 in the control unit 110 is described.
  • the inspection device 100 does not have to be provided with the correction unit 150.
  • the above-mentioned estimation model 121 is constructed by an apparatus including at least an acquisition unit 140 having a marker data acquisition unit 141 and a preparation data acquisition unit 142 and a correction unit 150, which exist independently of the inspection device 100. It may be.
  • the inspection device 100 can use the estimation model 121 by reading the estimation model 121 stored in the storage medium.
  • the inspection device 100 receives the estimation model 121 from another device via a wired or wireless network, so that the estimation model 121 can be used in the inspection device 100.
  • 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 for transmitting / receiving data to / from the inspection device 100 by wire or wirelessly.
  • the control unit 220 controls the terminal device 200 in an integrated manner.
  • the display unit 240 is a display capable of displaying 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, or the like. When the input device 230 is a touch panel, the touch panel is provided on the display unit 240. The user can use the function of the inspection device 100 via the terminal device 200.
  • the inspection device 100 is a device suitable for carrying out the inspection method according to the present embodiment described above.
  • control block control unit 110, particularly acquisition unit 140, correction unit 150, and discrimination unit 160 of the inspection device 100 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like. , May be realized by software.
  • the inspection device 100 includes a computer that executes a program instruction, which is software that realizes each function.
  • This computer includes, for example, at least one processor (control device) and at least one computer-readable recording medium that stores the program. Then, in the computer, the processor reads the program from the recording medium and executes it, thereby achieving the object of the present invention.
  • the processor for example, a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) can be used.
  • the recording medium in addition to a “non-temporary 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 can be used.
  • a RAM RandomAccessMemory
  • the program may be supplied to the computer via an arbitrary transmission medium (communication network, broadcast wave, etc.) capable of transmitting the program.
  • a transmission medium communication network, broadcast wave, etc.
  • one aspect of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the above program is embodied by electronic transmission.
  • the test method according to the present invention is a test method for testing a disease using a disease marker, and is marker data showing a measurement result of measuring a disease marker in a body fluid sample collected from a subject, and marker data of the body fluid sample.
  • the correction step of correcting the acquired marker data using the acquired preparation data and acquiring the corrected marker data, and the corrected marker data is a test method for testing a disease using a disease marker, and is marker data showing a measurement result of measuring a disease marker in a body fluid sample collected from a subject, and marker data of the body fluid sample.
  • the correction step of correcting the acquired marker data using the acquired preparation data and acquiring the corrected marker data, and the corrected marker data Based on this, the corrected marker data estimates the value of the marker data under predetermined preparation conditions of the same type as the index in the acquired preparation data, including a discrimination step of determining the presence or absence of disease in the subject. It was done.
  • the correction step uses a regression equation created by regression analysis using a plurality of the above-mentioned preparation data of the same index and a plurality of the above-mentioned marker data corresponding to each preparation data. It is used to correct the above marker data.
  • the prepared data is the information of the subject, the information indicating the collection conditions of the body fluid sample, and the treatment applied to the body fluid sample before the measurement of the disease marker. Contains at least one piece of information selected from the conditional information of.
  • the treatment conditions applied to the body fluid sample are the time until the body fluid sample is cryopreserved, the temperature at the time of cryopreservation, the time during which the body fluid sample is cryopreserved, and. It is at least one condition selected from the time until the body fluid sample is centrifuged.
  • the above information indicating the collection conditions of the body fluid sample is the thickness of the needle used for collecting the body fluid sample, and the blood collection tube used for collecting the body fluid sample. And at least one piece of information selected from the time from the final eating and drinking in the subject to the collection of the body fluid sample.
  • the prepared data includes information on the subject, and the information is information on the blood cell volume of the subject.
  • the above information of the subject is information regarding the race of the subject.
  • the body fluid sample is blood, serum, plasma, cerebrospinal fluid, urine, saliva, tears, interstitial fluid or lymph.
  • the body fluid sample is blood, serum or plasma.
  • the disease marker is miRNA.
  • the marker data is data obtained from microarray, PCR or sequencing.
  • the testing device is a testing device that tests for a disease using a disease marker, and marker data showing the result of measuring a disease marker in a body fluid sample collected from a subject, and preparation of the body fluid sample.
  • the data acquisition unit for acquiring the preparation data indicating the conditions and the correction unit for correcting the acquired marker data using the acquired preparation data and acquiring the corrected marker data are provided, and the corrected marker data is provided. Is an estimate of the value of the marker data under predetermined preparation conditions of the same type as the index in the acquired preparation data.
  • a discriminating unit for determining whether or not a disease is present in the subject is further provided based on the corrected marker data.
  • the inspection device may be realized by a computer.
  • the inspection device is realized by the computer by operating the computer as each part (software element) included in the inspection device. Inspection programs and computer-readable recording media on which they are recorded also fall within the scope of the present invention.
  • sample group 1 As a sample, 300 ⁇ L of each serum obtained from each of the above 84 persons was used. Total RNA was obtained from each serum using a reagent for RNA extraction in 3D-Gene (registered trademark) RNA extraction reagent from liquid sample kit (Toray Co., Ltd. (Japan)) according to the protocol specified by the company.
  • 3D-Gene registered trademark
  • RNA extraction reagent from liquid sample kit (Toray Co., Ltd. (Japan)
  • RNA obtained from each of the above 84 sera was fluorescently labeled with 3D-Gene (registered trademark) miRNA Labeling kit (Toray Industries, Inc.) based on the protocol defined by the company. ..
  • 3D-Gene registered trademark
  • Human miRNA Oligo chip equipped with a probe having a sequence complementary to miRNA registered in miRBase release 21 is used, and is based on a protocol defined by the company. Hybridization and washing after hybridization were performed under stringent conditions.
  • the DNA chip was scanned using a 3D-Gene (registered trademark) scanner (Toray Industries, Inc.), images were acquired, and the fluorescence intensity was quantified by 3D-Gene (registered trademark) Extension (Toray Industries, Inc.).
  • the expression level of the gene detected as follows was calculated using the quantified fluorescence intensity. First, excluding 5% each of the maximum and minimum signal intensities of multiple negative control spots, the [mean value + 2 x standard deviation] was calculated, and genes showing signal intensities greater than this value were considered to have been detected. rice field. In addition, the average value of the signal intensity of the negative control spot excluding 5% each of the maximum rank and the minimum rank is subtracted from the signal intensity of the detected gene, and the value after the subtraction is converted to a logarithmic value having a base of 2. The gene expression level was used.
  • sample group 2 As a sample, 300 ⁇ L of each serum obtained from each of the above 41 persons was used, and total RNA was obtained in the same manner as in Reference Example 1. Hereinafter, the sample group in Reference Example 2 will be referred to as sample group 2.
  • sample group 3 As a sample, 300 ⁇ L of each serum obtained from the 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 will be referred to as sample group 3.
  • Example 1 ⁇ Regression analysis of time required from blood collection to frozen storage and miRNA gene expression level>
  • linear regression analysis was performed as an example of regression analysis.
  • a linear regression analysis was performed using the time required from blood collection to storage at -80 ° C and the gene expression level of miRNA of the serum obtained in Reference Example 3, and changes in the gene expression level of miRNA per unit time. Obtained the coefficient.
  • y is the gene expression level of miRNA
  • x is the time (h) required from blood collection to storage at ⁇ 80 ° C.
  • Example 2 ⁇ Verification of pancreatic cancer and biliary tract cancer discrimination performance 1>
  • correction was made using the coefficient of variation of the gene expression level of miR-4678-3p obtained in Example 1, and the time required for the sample group 1 from blood collection to storage at -80 ° C was 0.
  • the estimated gene expression level in 5 hours was determined, and the discrimination performance between cancer patients and healthy subjects was confirmed.
  • 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).
  • the following step-by-step procedure was taken to distinguish between pancreatic cancer and biliary tract cancer. That is, for each sample in the sample group 1, the gene expression level of miR-4678-3p, the time required from blood collection to storage at -80 ° C, and the change coefficient (-0.5050) obtained in Example 1 were used.
  • the estimated gene expression level was calculated 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 information on the presence or absence of morbidity, 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 FIG. In FIG. 2 and FIG. 3 described later, the "true positive rate” represents the ratio of those correctly judged to be positive by the test, that is, the sensitivity.
  • the "false positive rate” is the percentage of those who are mistakenly judged to be positive by the test, and is calculated as 1- (specificity). The specificity refers to the rate at which a negative test is correctly judged to be negative.
  • Example 3 ⁇ Verification of pancreatic cancer and biliary tract cancer discrimination performance 2>
  • correction was made using the coefficient of variation of the gene expression level of miR-4678-3p obtained in Example 1, and the time required for the sample group 2 from blood collection to storage at -80 ° C was 0.
  • the estimated gene expression level in the case of 5 hours was obtained, and the discrimination performance between the cancer patient and the healthy subject was confirmed in the same manner as in Example 2.
  • the following step-by-step procedure was taken to distinguish between pancreatic cancer and biliary tract cancer. That is, for each sample in the sample group 2, the gene expression level of miR-4678-3p, the time required from blood collection to storage at ⁇ 80 ° C., and the change coefficient ( ⁇ 0.5050) obtained in Example 1 were used. The gene expression level of each sample was calculated in the same manner as in Example 2 when the time required from blood collection to storage at ⁇ 80 ° C. was 0.5 hour. An ROC curve was created based on the calculated estimated gene expression level and information on the presence or absence of morbidity, and the AUC was calculated. As a result, the AUC was 0.8720. The created ROC curve is shown in FIG.
  • the ROC curve was created using the gene expression level of each miR-4678-3p in the sample group 1, and the AUC was obtained. As a result, the AUC was 0.9117.
  • the created ROC curve is shown in FIG.
  • Example 4 ⁇ Regression analysis of white blood cell count and miRNA gene expression>
  • linear regression analysis was performed as an example of regression analysis.
  • a linear regression analysis was performed using the white blood cell count (logarithmic value of the base 2) and the gene expression level of miRNA in the serum obtained in Reference Example 4, and the change coefficient of the gene expression level of miRNA per unit time was obtained. ..
  • y is the gene expression level of miRNA
  • x is the white blood cell count.
  • Example 5 ⁇ Verification of pancreatic cancer and biliary tract cancer discrimination performance 3>
  • correction was performed using the coefficient of variation of the gene expression level of miR-6778-5p obtained in Example 4, and the white blood cell count (log of base 2) of the sample group 5 was 12.4.
  • the estimated gene expression level in the case was determined, and the discrimination performance between cancer patients and healthy subjects was confirmed. Specifically, for the calculated estimated gene expression level, the average value of 3 cancer patients and the average value of 3 healthy subjects were obtained and tested by Welch's t-test.
  • the estimated gene expression level was calculated when the white blood cell count (radix of the base 2) was 12.4.
  • 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. ..
  • the difference in the average value of the estimated gene expression levels was 0.71.
  • Welch's t-test showed that p ⁇ 0.01, indicating that there was a significant difference.
  • the difference in the average value of gene expression in each group before the correction was 0.47.
  • FIG. 4 Each graph (box plot) comparing the average values is shown in FIG. In FIG. 4, the left side is a graph before the correction is performed, and the right side is a graph after the correction is performed.
  • the present invention can be used for a disease test using a disease marker.
  • Inspection device 100 Inspection device 110 Control unit 120 Storage unit 121 Estimated model 130 Communication unit 140 Acquisition unit 141 Marker data acquisition unit 142 Preparation data acquisition unit 150 Correction unit 160 Discrimination unit

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Abstract

L'invention concerne un procédé hautement précis pour analyser une maladie sans imposer une charge excessive dans un contexte médical. Le procédé pour effectuer un examen d'une maladie à l'aide de marqueurs de maladie comprend une étape d'acquisition de données de marqueur montrant les résultats de mesure de marqueurs de maladie mesurés dans un échantillon de fluide corporel prélevé chez un sujet et des données de préparation montrant les conditions de préparation de l'échantillon de fluide corporel ; une étape consistant à corriger les données de marqueur à l'aide des données de préparation ; et une étape consistant à évaluer si le sujet a ou non contracté la maladie sur la base des données de marqueur corrigées.
PCT/JP2021/003311 2020-01-30 2021-01-29 Procédé, dispositif et programme d'examen WO2021153753A1 (fr)

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Citations (2)

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Publication number Priority date Publication date Assignee Title
WO2018041726A1 (fr) * 2016-08-27 2018-03-08 Academisch Medisch Centrum Biomarqueurs de détermination de la présence d'une plaque d'athérome instable
WO2018175759A1 (fr) * 2017-03-21 2018-09-27 Quadrant Biosciences Inc. Analyse de trouble du spectre autistique

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Publication number Priority date Publication date Assignee Title
WO2018041726A1 (fr) * 2016-08-27 2018-03-08 Academisch Medisch Centrum Biomarqueurs de détermination de la présence d'une plaque d'athérome instable
WO2018175759A1 (fr) * 2017-03-21 2018-09-27 Quadrant Biosciences Inc. Analyse de trouble du spectre autistique

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GEORGIADIS, PANAGIOTIS ET AL.: "Evolving DNA methylation and gene expression markers of B- cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis", GENOMICS, vol. 18, no. 728, 2017, pages 1 - 15, XP021248920, DOI: 10.1186/s12864-017-4117-4 *
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