[go: up one dir, main page]

WO2025089029A1 - Procédé de détermination, dispositif de détermination, système de détermination, programme de détermination et support d'enregistrement - Google Patents

Procédé de détermination, dispositif de détermination, système de détermination, programme de détermination et support d'enregistrement Download PDF

Info

Publication number
WO2025089029A1
WO2025089029A1 PCT/JP2024/035849 JP2024035849W WO2025089029A1 WO 2025089029 A1 WO2025089029 A1 WO 2025089029A1 JP 2024035849 W JP2024035849 W JP 2024035849W WO 2025089029 A1 WO2025089029 A1 WO 2025089029A1
Authority
WO
WIPO (PCT)
Prior art keywords
amount
determination
addition
data
small rnas
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/JP2024/035849
Other languages
English (en)
Japanese (ja)
Inventor
俊介 小幡
瑠衣沙 西田
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Arkray Inc
Original Assignee
Arkray Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Arkray Inc filed Critical Arkray Inc
Publication of WO2025089029A1 publication Critical patent/WO2025089029A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/113—Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing
    • 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
    • C12Q1/6806—Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
    • 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
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor

Definitions

  • This disclosure relates to a determination method, a determination device, a determination system, a determination program, and a recording medium.
  • Japanese Patent No. 7021097 discloses a disease prevalence assessment device that includes a sample data acquisition unit and a prevalence assessment unit.
  • the sample data acquisition unit acquires sample data including the expression levels of multiple types of miRNA in a sample derived from a living body.
  • the prevalence assessment unit uses a trained model to output prevalence assessment results for the multiple diseases in the multiple body parts for the acquired sample data.
  • the trained model is a trained model that can assess the prevalence of each of the multiple diseases, including multiple malignant diseases or multiple benign diseases, including cases where the patient is suffering from multiple diseases, obtained in advance by machine learning using training data including multiple sample data having items for identifying the presence or absence of multiple diseases in multiple body parts.
  • WO 2021/132547 discloses a testing method for testing for a disease using a disease marker, the testing method including a specimen data acquisition step and a discrimination step.
  • the specimen data acquisition step acquires marker data indicating the 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.
  • the discrimination step determines the presence or absence of a disease in the subject by inputting the marker data and preparation data acquired in the specimen data acquisition step into a trained model that has been machine-learned to learn the correlation between a set of marker data indicating the results of measuring a disease marker in a body fluid sample and preparation data indicating the preparation conditions of the body fluid sample, and the presence or absence of a disease in the subject from which the body fluid sample was collected.
  • Non-Patent Document 1 discloses that, using 20 types of fluorescently labeled synthetic miRNAs, the reaction efficiency of each miRNA in a 3' ligation enzymatic reaction in which a nucleic acid sequence is added to the 3' end of the miRNA was investigated under multiple conditions in which the type of ligation enzyme, the amount of PEG reagent added, the reaction time, etc. were varied, and the results showed that the efficiency of the miRNA ligation reaction may vary depending on the PEG concentration.
  • Non-patent document 1 Song Y, Liu KJ, Wang TH. Elimination of ligation dependent artifacts in T4 RNA ligase to achieve high efficiency and low bias microRNA capture. PLoS One. 2014 Apr 10
  • PEG reagents have a relatively high viscosity, and there is a tendency for the differences in technique between people who add the PEG reagent to be large. For this reason, there is a tendency for the amount of PEG reagent added to vary.
  • the present disclosure aims to provide a determination method, determination device, determination system, determination program, and recording medium that can determine with high accuracy the amount of PEG reagent added when ligating an adapter and a PEG reagent to the 5' or 3' end of multiple small RNAs.
  • the determination method is a method for determining the amount of PEG reagent added used when performing ligation on multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, and includes an addition amount determination step for determining the amount added from the small RNA data using a determination criterion based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added used when performing ligation on the multiple small RNAs in the biological sample.
  • FIG. 2 is a flow chart showing each step of a determination method according to the present embodiment.
  • FIG. 2 is a schematic block diagram of an example of a computer that functions as a determination device according to the present embodiment.
  • 1 is a block diagram showing a functional configuration of a determination device according to an embodiment of the present invention;
  • 13 is a table showing the judgment results in additive amount learned models 1 to 4 of the present embodiment.
  • FIG. 2 is a conceptual diagram illustrating a continuous value regression model according to the present embodiment.
  • 11 is a graph showing the determination results in the continuous value regression model of the present embodiment.
  • Fig. 1 is a schematic diagram showing each step of the determination method 10 according to the present embodiment.
  • Determination method 10 is a method for determining the amount of PEG reagent added (hereinafter, sometimes referred to as the amount of PEG added) used when performing ligation on multiple small RNAs in a biological sample collected from a subject, using small RNA data that shows the results of measuring the expression levels of the multiple small RNAs.
  • biological samples collected from subjects include body fluids such as blood, serum, urine, tears, saliva, sweat, semen, lymph, tissue fluid, body cavity fluid (e.g., pleural fluid, ascites, etc.), cerebrospinal fluid, amniotic fluid, vaginal fluid, and nasal mucus.
  • the biological sample may be any sample that can be collected from a living body and from which the expression levels of multiple small RNAs can be measured.
  • subject refers to the subject from which the biological sample is collected.
  • the subject from which the biological sample is collected may be either a human or a non-human animal. Examples of non-human animals include non-human mammals (monkeys, dogs, cats, mice, rats, rabbits, cows, horses, pigs, and sheep), and birds (chickens, quails, etc.).
  • microRNA An example of a small RNA is microRNA.
  • the small RNA may be a small RNA other than microRNA (e.g., piRNA and tsRNA). Ligation and PEG reagents will be described later.
  • the determination method 10 includes a collection step 11, a ligation step 13, a measurement step 12, an addition amount determination step 14, and a disease determination step 16.
  • the collection step 11, the ligation step 13, the measurement step 12, the addition amount determination step 14, and the disease determination step 16 are performed in this order, for example.
  • the determination method 10 is a method having a sampling step 11, a measurement step 12, and a disease determination step 16, it can also be called a sampling method, a measurement method, and a disease determination method. Furthermore, if analysis, testing, etc. is performed based on the determination in the disease determination step 16, it can also be called an analysis method, a testing method, etc. Each step of the determination method 10 will be explained below.
  • the collecting step 11 is a step of collecting a biological sample from a subject.
  • the collecting step 11 is performed before the measurement step 12.
  • a collection process is performed as follows.
  • blood is collected using a vacuum blood collection tube containing a serum separating agent (blood collection process).
  • blood is mixed by inversion.
  • the blood is then allowed to coagulate at room temperature for at least 30 minutes.
  • the mixture is centrifuged and the serum is separated (centrifugation treatment).
  • the serum is separated and stored at ⁇ 80° C. (storage treatment).
  • the process from collection of a biological sample from a subject to completion of the centrifugation operation (specifically, the process from the blood collection process to the preservation process described above) is performed within a predetermined time (e.g., 2 hours).
  • the centrifugation operation is, for example, the process from the centrifugation process to the preservation process described above.
  • Ligation is an enzymatic reaction in which a base sequence called an adapter is bound to the 5' or 3' end of a nucleic acid such as DNA or RNA including a small RNA.
  • the ligation step 13 is a step in which a base sequence called an adapter is bound to the 5' or 3' end of a small RNA.
  • a PEG reagent is used when performing the ligation.
  • the ligation step 13 is performed, for example, in the preparation of a library for NGS.
  • the PEG reagent is a reagent that contains PEG (PolyEthylene Glycol). Any PEG reagent that contains at least PEG can be used, and it is possible to use one that contains other components.
  • PEG is composed of a polymer of the monomer Ethylene Glycol, and there are several types of PEG due to differences in the degree of polymerization and average molecular weight (for example, PEG 200, PEG 300, PEG 400, PEG 600, PEG 1000, PEG 2000, PEG 4000, PEG 6000, PEG 8000, PEG 10000, PEG 20000, PEG 500000, PEG 2000000, PEG 4000000, etc.).
  • PEG functions as a catalyst to promote the enzymatic reaction in ligation, and reduces the bias in reactivity of each small RNA.
  • a reagent containing the following components can be used. Nuclease-free water T4 RNA Ligase Reaction Buffer(NEB/M0242) 100% DMSO (13445-74/Nacalai Tesque) 50% PEG (NEB/B1004) Total RNA
  • the measurement step 12 is a step of measuring the expression levels of multiple small RNAs in the subject's biological sample.
  • a next-generation sequencer (NGS) is used as a measurement device to measure multiple small RNAs contained in the subject's biological sample (for example, in serum) and identify the base sequence of each small RNA.
  • NGS next-generation sequencer
  • the number of identified small RNAs is counted for each base sequence to obtain the number of reads of the small RNA in the NGS. This number of reads of the small RNA corresponds to the expression level (specifically, the absolute expression level) of the small RNA.
  • this number of reads of the small RNA means small RNA data showing the result of measuring the expression levels of multiple small RNAs in the subject's biological sample.
  • the expression levels of multiple small RNAs for example, microRNAs
  • a biological sample for example, serum
  • the expression levels of small RNAs may be expressed as relative values by processing the data from the NGS measurement results, and such relative values also refer to small RNA data.
  • the number of reads of small RNA output by NGS may be normalized to obtain a relative expression level (i.e., relative expression level).
  • a relative expression level i.e., relative expression level
  • RPM Read Per Million
  • the small RNA data may indicate a normalized relative expression level.
  • the small RNA data may be an absolute value quantified as an absolute value.
  • NGS makes it possible to simultaneously measure the expression levels of multiple small RNAs in multiple biological samples (e.g., serum samples collected from multiple subjects). That is, in measurement step 12, the expression levels of multiple small RNAs are measured for multiple biological samples in the same process.
  • next-generation sequencers other measuring devices such as DNA chips, quantitative PCR, and flow cytometers can also be used as long as the expression levels of multiple small RNAs can be measured.
  • various methods including publicly known methods, can be used to measure the expression levels of multiple small RNAs.
  • the amount of PEG reagent added is determined using small RNA data indicating the results of measuring the expression levels of the plurality of small RNAs in the amount determination step 14.
  • the determination of the amount of PEG reagent added includes a concept of determining whether the amount is appropriate and a concept of determining the amount itself.
  • the small RNA data obtained in the measurement step 12 is input into the addition amount trained model to determine whether the amount of PEG reagent to be added is appropriate.
  • the criterion for determining whether the amount of PEG reagent to be added is appropriate is set, for example, based on whether a correct judgment can be made in the disease assessment process 16. If a correct judgment can be made in the disease assessment process 16 even if the amount of PEG reagent added is low, the criterion becomes one that allows a low amount to be added.
  • the allowable amount of addition varies depending on the disease to be judged in the disease judgment process 16, and the judgment criteria in the addition amount judgment process 14 also vary correspondingly. Furthermore, the judgment process is not limited to disease judgment, and various judgments can be applied, and the allowable amount of addition varies depending on the disease to be judged, and the judgment criteria in the addition amount judgment process 14 also vary correspondingly.
  • the added amount trained model used in the added amount determination step 14 is generated, for example, as follows: That is, the added amount trained model is generated by machine learning the correlation between measurement data indicating the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects, and added amount data indicating the added amount of PEG reagent used when performing ligation on the multiple small RNAs in the biological samples collected from the multiple subjects.
  • the measurement data is the result of measuring multiple small RNAs in biological samples collected from multiple subjects.
  • the multiple subjects may include subjects with a disease and subjects without a disease (i.e., healthy subjects), or may include only subjects with a disease and subjects without a disease (i.e., healthy subjects).
  • the measurement data is obtained by the same measurement method as the small RNA data described above.
  • a next-generation sequencer (NGS) is used as a measurement device to measure multiple small RNAs contained in a biological sample from a subject, and to identify the base sequence of each small RNA.
  • NGS next-generation sequencer
  • the number of identified small RNAs is counted for each base sequence to determine the number of small RNA reads in the NGS.
  • This number of small RNA reads corresponds to the expression level (specifically, absolute expression level) of the small RNA. In other words, this number of small RNA reads becomes the measurement data.
  • the amount of PEG added data is specifically data that indicates whether the amount of PEG added related to the measurement data is appropriate.
  • the amount of PEG added data can be data that indicates that an amount of PEG added that allows a correct judgment to be performed in the disease assessment process 16 is appropriate (good), and that an amount of PEG added that does not allow a correct judgment to be performed in the disease assessment process 16 is inappropriate (bad).
  • a trained model for the amount of PEG added can be generated using measurement data obtained from biological samples collected from multiple subjects under conditions in which the amount of PEG added is appropriate, and measurement data obtained from biological samples collected from multiple subjects under conditions in which the amount of PEG added is inappropriate.
  • the disease of the subject that is machine-learned by the additive amount trained model used in the additive amount determination step 14 may include the disease determined in the disease determination step 16. That is, if the disease determined in the disease determination step 16 is a cancer disease, the measurement data of a sample from a subject with a cancer disease is included. Note that the type of cancer disease determined in the disease determination step 16 and the cancer disease of the measurement data may be the same or different. Therefore, for example, if the disease determined in the disease determination step 16 is pancreatic cancer, the measurement data of a sample from a subject with lung cancer may be used as training data.
  • measurement data from the top 100 small RNAs with the highest expression levels in biological samples from multiple subjects is used as training data.
  • measurement data is used that excludes small RNAs with relatively low expression levels.
  • Small RNAs with relatively low expression levels have the advantage that they can easily function as explanatory variables, since even small fluctuations in expression level result in large fold changes.
  • they also have the disadvantage of being easily affected by fluctuations due to factors other than the element of interest (specifically, the amount of PEG added) and fluctuations due to measurement errors, making them less robust. Therefore, by using only small RNAs with relatively high expression levels as training data, the effect of strengthening robustness can be achieved.
  • the measurement data excluding small RNAs whose expression level was zero in the biological samples of any of the subjects is used as training data. Since the variation from zero is an infinite fold change, which is difficult to quantify and is particularly susceptible to variations due to factors other than the amount of PEG added and variations due to measurement errors, small RNAs whose expression level is zero in any of the samples used as training data are excluded from the training data.
  • the learning model is made to qualitatively learn whether the amount of PEG to be added is appropriate, thereby generating an added amount trained model.
  • the added amount trained model judges whether the amount of PEG to be added is appropriate, and outputs this suitability, thereby judging whether the amount of PEG to be added is appropriate in the added amount judgment process 14. Therefore, the added amount judgment process 14 displays a judgment result that the amount of PEG to be added is appropriate, or a judgment result that the amount of PEG to be added is inappropriate.
  • the learning model is trained to learn whether the amount of PEG added is appropriate, and an additive amount trained model that performs a binary judgment is generated, but this is not limited to this.
  • an additive amount trained model may be generated so that the amount of PEG added is judged as one of three values: appropriate (good), warning, and inappropriate (bad).
  • the learning model is trained on the appropriateness of the amount of PEG added qualitatively to generate the learned model of the amount of PEG added, but this is not limited to the above.
  • the learning model may be trained on the amount of PEG added quantitatively to generate the learned model of the amount of PEG added.
  • the amount of PEG added is judged by the learned model of the amount of PEG added, and the judgment result of the amount of PEG added is output, for example, as a judgment value.
  • the judgment value here is not a binary value of 0 or 1, but a value having a predetermined numerical range.
  • the appropriateness of the amount of PEG added is judged based on the judgment result of the judgment unit that judges the appropriateness of the amount of PEG added based on a comparison between the output judgment value and a threshold value. That is, in the addition amount judgment process 14, if the judgment unit judges that the output judgment value is equal to or greater than the threshold value, the judgment result that the amount of PEG added is inappropriate is displayed, and if the judgment unit judges that the output judgment value is less than the threshold value, the judgment result that the amount of PEG added is appropriate is displayed.
  • the amount of PEG added is determined using an addition amount trained model that has been machine-learned to determine the correlation between the measurement data and the addition amount data, but this is not limited to this.
  • the amount of PEG added may be determined using a regression model that determines the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when performing ligation on multiple small RNAs in the biological samples collected from the multiple subjects.
  • the amount of PEG to be added is determined by inputting small RNA data into a regression model in the amount-to-add determination step 14. Specifically, in the amount-to-add determination step 14, for example, the small RNA data is input into a regression model to output a value for the amount of PEG to be added.
  • the determination unit determines that the output determination value is equal to or greater than the threshold value, the determination result that the amount of PEG to be added is inappropriate is displayed, and if the determination unit determines that the output determination value is less than the threshold value, the determination result that the amount of PEG to be added is appropriate is displayed.
  • the addition amount determination step 14 is not limited to the use of the above-mentioned addition amount learned model and the above-mentioned regression model, and it is possible to use a determination criterion based on various algorithms.
  • the determination criterion used in the addition amount determination step 14 is a determination criterion based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when ligating multiple small RNAs in the biological samples, and it is sufficient that the addition amount can be determined from the small RNA data.
  • the disease determination step 16 is a step of determining the presence or absence of a disease. Specifically, in the disease determination step 16, the small RNA data obtained in the measurement step 12 is input to a disease-trained model that has been machine-learned to determine the correlation between measurement data showing the results of measuring a plurality of small RNAs in a biological sample and disease data showing the presence or absence of a disease in a subject from whom the biological sample was collected, thereby determining the presence or absence of a disease.
  • the disease assessment step 16 if the result of the assessment in the addition amount assessment step 14 is that the amount of PEG added is appropriate, the presence or absence of a disease is assessed. In other words, if the result of the assessment in the addition amount assessment step 14 is that the amount of PEG added is inappropriate, the disease assessment step 16 is not executed. In this case, for example, the result of the assessment that the amount of PEG added is inappropriate may be presented to the user (i.e., the person who executes the assessment method).
  • the disease determined by the disease discrimination trained model is the same as the disease used for training in the additive amount trained model.
  • the disease used for training in the additive amount trained model is cancer
  • the disease determined by the disease discrimination trained model is also cancer.
  • the disease type of the measurement data used as training data in the disease discrimination trained model is the same as the disease type of the measurement data used as training data in the additive amount trained model.
  • the disease determination step 16 was not executed, but this is not limited to the above. For example, even if the addition amount determination step 14 determined that the amount of PEG added was inappropriate, the disease determination step 16 may be executed if the determination result is to be obtained as reference data, for example. In this case, for example, after the determination result that the amount of PEG added was inappropriate is obtained, the fact that the determination was made may be presented to the user. Also, in this embodiment, the disease determination step 16 was executed after the addition amount determination step 14, but it may be executed before the addition amount determination step 14. In this case, if the addition amount determination step 14 determined that the amount of PEG added was inappropriate, the determination result of the disease determination step 16 is treated as reference data, for example.
  • the determination system 20 includes a measurement device 21 and a determination device 30, as shown in FIG.
  • the measurement device 21 is an example of a measurement unit, and is a device that executes the above-mentioned measurement step 12. That is, the measurement device 21 measures the expression levels of multiple small RNAs contained in each of multiple samples.
  • the measurement device 21 for example, an NGS is used.
  • the determination device 30 is an example of a determination unit.
  • the determination device 30 is a device that executes the above-mentioned addition amount determination step 14. That is, the determination device 30 acquires small RNA data indicating the results of measuring a plurality of small RNAs in a biological sample of a subject, and inputs the acquired small RNA data into an addition amount trained model to determine the amount of PEG added to the biological sample collected from the subject.
  • the determination device 30 executes the disease determination step 16 described above. That is, the determination device 30 determines the presence or absence of a disease by inputting the small RNA data obtained in the measurement step 12 into a disease-trained model that has been machine-learned to determine the correlation between measurement data showing the results of measuring multiple small RNAs in a biological sample and disease presence data showing the presence or absence of a disease in the subject from whom the biological sample was collected.
  • the determination device 30 functions as a computer, and as shown in FIG. 2, has a CPU (Central Processing Unit) 31, a ROM (Read Only Memory) 32, a RAM (Random Access Memory) 33, a storage 34, an input unit 35, a display unit 36, and a communication interface (I/F) 37. Each component is connected to each other via a bus 39 so that they can communicate with each other.
  • CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • CPU 31 (an example of a processor) is a central processing unit that executes various programs and controls each part. That is, CPU 31 reads a program from ROM 32 or storage 34, and executes the program using RAM 33 as a working area. CPU 31 controls each of the above components and performs various calculation processes according to the program stored in ROM 32 or storage 34.
  • CPU 31 is an example of a processor.
  • ROM 32 records various programs and various data.
  • RAM 33 temporarily stores programs or data as a working area.
  • Storage 34 is composed of a HDD (Hard Disk Drive) or SSD (Solid State Drive), and records various programs including the operating system, and various data.
  • a judgment program for executing a judgment process that performs the above-mentioned judgment method is recorded in storage 34.
  • the judgment program may be a single program, or may be a group of programs consisting of multiple programs or modules.
  • the judgment program may be recorded in ROM 32.
  • ROM 32 and storage 34 function as an example of a non-transitory recording medium.
  • processors are not limited to the aforementioned CPU, which is a general-purpose processor, but may be, for example, a dedicated processor made up of a circuit designed specifically to execute a specific process. Also, an example of a processor is not limited to a single processor, but may be a processor made up of multiple processors working together at physically separate locations.
  • the input unit 35 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs.
  • the input unit 35 also receives as input information on the expression levels of multiple small RNAs measured by the measurement device 21.
  • the display unit 36 is, for example, a liquid crystal display, and displays various information.
  • the determination device 30 can present the determination result of the amount of PEG added and the determination result of the presence or absence of a disease to the user through the display unit 36.
  • the display unit 36 may also function as the input unit 35 by adopting a touch panel system.
  • the communication interface 37 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI (Fiber Distributed Data Interface), and Wi-Fi (registered trademark).
  • the CPU 31 executes the judgment program to function as a judgment function unit 160 and a disease judgment unit 170.
  • the judgment function unit 160 executes the aforementioned addition amount judgment step 14. That is, in the addition amount judgment step 14, the judgment function unit 160 inputs the small RNA data obtained by the measurement device 21 into the addition amount learned model to judge whether the amount of PEG added to the biological sample collected from the subject is appropriate (see the aforementioned addition amount judgment step 14).
  • the disease determination unit 170 executes the disease determination step 16. That is, the disease determination unit 170 determines the presence or absence of a disease by inputting the small RNA data obtained by the measurement device 21 into a disease-trained model that has been machine-learned to determine the correlation between measurement data showing the results of measuring multiple small RNAs in a biological sample and disease data showing the presence or absence of a disease in the subject from whom the biological sample was collected (see the disease determination step 16 described above).
  • the CPU 31 may perform processing to output the determination result of the amount of PEG reagent added and the determination result of the presence or absence of a disease. This processing is performed, for example, by displaying on the display unit 36 and transmitting to an external device (e.g., the cloud).
  • an external device e.g., the cloud
  • the determination system 20 includes the measurement device 21 and the determination device 30, but the determination system 20 may be configured with a single device.
  • the single device functions as an example of the measurement unit and the determination unit.
  • the determination device 30 may also be composed of multiple devices.
  • the determination device 30 may be composed of multiple (e.g., two) devices that share the functions of the addition amount determination step 14 and the disease determination step 16 described above.
  • the amount of PEG reagent to be added is determined by inputting the small RNA data obtained in the measurement step 12 into an addition amount trained model that has machine-learned the correlation between measurement data indicating the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data indicating the amount of PEG reagent added when performing ligation on the multiple small RNAs in the biological samples.
  • the amount of addition is determined by inputting small RNA data into an addition amount trained model that has been machine-learned to learn the correlation between measurement data and addition amount data, so the amount of PEG reagent added can be determined with a high degree of accuracy compared to, for example, determination by visual inspection or measurement using a measurement kit.
  • the amount of PEG reagent added can be determined with high accuracy, so highly accurate information on the amount of PEG reagent added can be obtained, and for example, in the disease determination process 16, there is no need to obtain information on the amount of PEG reagent added by inputting it by the user.
  • serum was obtained as a biological sample from 24 healthy subjects by performing the collection step 11 within 2 hours.
  • the serum was subjected to the ligation step 13 using five PEG reagent formulations in which the amount of PEG was increased or decreased based on the basic formulation below, and a total of 120 samples (24 subjects x 5 formulations) were obtained.
  • the expression levels of multiple small RNAs were measured using NGS, and small RNA data showing the measurement results were obtained.
  • small RNAs with zero expression levels were excluded, and small RNA data for the top 100 small RNAs with the highest expression levels was used.
  • the small RNA data for the 120 samples the small RNA data for 100 samples (20 people x 5 prescriptions) was used as training data, and the small RNA data for 20 samples (4 people x 5 prescriptions) was used as evaluation data.
  • Formulation 1 The amount of PEG was reduced by 50% in the basic formulation.
  • Formulation 2 The amount of PEG was reduced by 25% in the basic formulation.
  • Formulation 3 The amount of PEG was unchanged in the basic formulation.
  • Formulation 4 The amount of PEG was increased by 25% in the basic formulation.
  • Formulation 5 The amount of PEG was increased by 50% in the basic formulation.
  • the additive amount trained model 1 is a model that performs a binary judgment in which the formula 1 is bad and the formulas 2 to 5 are good.
  • the additive amount trained model 2 is a model that performs a binary judgment in which the recipes 1 and 2 are treated as bad, and the recipes 3 to 5 are treated as good.
  • the additive amount trained model 3 is a model that performs a binary judgment in which the recipes 1 to 3 are bad and the recipes 4 and 5 are good.
  • the additive amount trained model 4 is a model that performs a binary judgment in which the formulas 1 to 4 are treated as bad and the formula 5 is treated as good.
  • ⁇ Example of learning model> Various linear and nonlinear algorithms known as machine learning algorithms can be used, or multiple algorithms can be combined. For example, the following algorithms can be used:
  • additive amount trained models 1 to 4 it is possible to estimate the amount of PEG added within a range that can be specified for prescriptions 1 to 5. For example, if additive amount trained model 2 judges the product as good and additive amount trained model 3 judges the product as bad, the amount of PEG added is estimated to be in the range of -25% to +25% of the basic prescription (1.0 ⁇ L).
  • a continuous value regression model was generated to estimate the percentage of the amount of PEG added relative to the basic formula (1.0 ⁇ L) using all of the training data obtained from formulations 1 to 5.
  • the continuous value regression model estimates whether the amount is -50%, -25%, 0%, +25%, or +50% relative to the basic formula (1.0 ⁇ L) (see FIG. 6).
  • the small RNA data obtained in the measurement step 12 is input to a disease-trained model that has been machine-learned to learn the correlation between the measurement data showing the results of measuring the expression levels of multiple small RNAs in a biological sample and the disease data showing the presence or absence of a disease in the subject from whom the biological sample was collected, thereby indicating the presence or absence of a disease. Therefore, it can be said that the measured expression level of the small RNA indicates the presence or absence of a disease based on the judgment criteria in the disease-trained model.
  • the determination method 10 is a determination method that determines the amount of PEG reagent used when ligating multiple small RNAs in a biological sample collected from a subject using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, and the expression level of the small RNA indicates the presence or absence of the disease based on the judgment criteria in the disease-trained model that has been machine-learned to learn the correlation between the measurement data showing the results of measuring the expression levels of multiple small RNAs in a biological sample and the disease data showing the presence or absence of a disease in the subject from whom the biological sample was collected.
  • a method for determining an amount of a PEG reagent used in ligating a plurality of small RNAs in a biological sample collected from a subject, using small RNA data showing a result of measuring the expression levels of the plurality of small RNAs comprising: The method includes an addition amount determination step of determining the amount of addition from the small RNA data based on a determination criterion based on a correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when performing ligation on the multiple small RNAs in the biological samples.
  • the addition amount determining step includes: The method according to aspect 1, further comprising inputting the small RNA data into an added amount trained model that has been machine-learned to determine a correlation between the measurement data and the added amount data, thereby determining the added amount.
  • the method according to aspect 2 further comprising inputting the small RNA data into the additive amount trained model to determine whether the additive amount is appropriate.
  • the method according to aspect 2 further comprising inputting the small RNA data into the additive amount trained model to output the additive amount.
  • the addition amount determination step includes: The method according to aspect 1, further comprising inputting the small RNA data into a regression model that determines a correlation between the measurement data and the data on the amount of addition, thereby determining the amount of addition.
  • the addition amount determination step includes: The method according to aspect 1, further comprising inputting the small RNA data into a regression model that determines a correlation between the measurement data and the data on the amount of addition, thereby determining the amount of addition.
  • the method according to aspect 6 further comprising inputting the small RNA data into the regression model to determine whether the amount of addition is appropriate.
  • (Aspect 10) a disease determination step of determining the presence or absence of a disease by inputting the small RNA data into a disease trained model that has been machine-learned to determine a correlation between measurement data showing the results of measuring a plurality of small RNAs in a biological sample and disease presence data showing the presence or absence of a disease in a subject from whom the biological sample was collected;
  • the expression level of the small RNA indicates the presence or absence of the disease based on a judgment criterion in a disease-trained model that machine-learns the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in a biological sample and disease occurrence data showing the presence or absence of the disease in the subject from whom the biological sample was collected.
  • a determination device that determines an amount of a PEG reagent used in ligating a plurality of small RNAs in a biological sample collected from a subject, using small RNA data indicating a result of measuring the expression levels of the plurality of small RNAs, comprising: A processor is provided.
  • the processor A determination device that determines the amount of addition from the small RNA data based on a determination criterion based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when performing ligation on multiple small RNAs in the biological samples.
  • a system for determining an amount of a PEG reagent used in ligating a plurality of small RNAs in a biological sample collected from a subject, using small RNA data showing a result of measuring the expression levels of the plurality of small RNAs comprising: A measurement unit that acquires small RNA data indicating the results of measuring a plurality of small RNAs in a biological sample of a subject; a determination unit that determines the amount of addition from the small RNA data based on a determination criterion based on a correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when ligating the multiple small RNAs in the biological samples; A determination system having the above configuration.
  • a process for determining an amount of a PEG reagent used in ligating a plurality of small RNAs in a biological sample collected from a subject, using small RNA data showing a result of measuring the expression levels of the plurality of small RNAs comprising: A determination program for executing a determination process that determines the amount of addition from the small RNA data based on a determination criterion based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when performing ligation on multiple small RNAs in the biological samples.
  • a process for determining an amount of a PEG reagent used in ligating a plurality of small RNAs in a biological sample collected from a subject, using small RNA data showing a result of measuring the expression levels of the plurality of small RNAs comprising: A non-temporary recording medium having recorded thereon a judgment program for executing a judgment process for determining the amount of addition from the small RNA data based on a judgment criterion based on a correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when performing ligation on multiple small RNAs in the biological samples.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Molecular Biology (AREA)
  • Genetics & Genomics (AREA)
  • Biomedical Technology (AREA)
  • Organic Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Biotechnology (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Hematology (AREA)
  • Biophysics (AREA)
  • Urology & Nephrology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Pathology (AREA)
  • General Physics & Mathematics (AREA)
  • Plant Pathology (AREA)
  • Medicinal Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Cell Biology (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

Procédé permet de déterminer la quantité d'ajout d'un réactif PEG utilisé dans la ligature par rapport à une pluralité de petites molécules d'ARN dans un échantillon biologique prélevé sur un sujet, par l'utilisation de données relatives aux petits ARN qui indiquent les résultats de la mesure des niveaux d'expression de la pluralité de petites molécules d'ARN. Le procédé de calcul comprend une étape de calcul de la quantité d'ajout pour déterminer la quantité d'ajout susmentionnée à partir des données susmentionnées sur les petites molécules d'ARN en utilisant des critères de détermination basés sur la relation entre les données de mesure qui indiquent les résultats de la mesure des niveaux d'expression d'une pluralité de petites molécules d'ARN dans des échantillons biologiques prélevés sur une pluralité de sujets et les données de quantité d'ajout qui indiquent les quantités d'ajout d'un réactif PEG utilisé dans la ligature en ce qui concerne la pluralité de petites molécules d'ARN dans les échantillons biologiques.
PCT/JP2024/035849 2023-10-24 2024-10-07 Procédé de détermination, dispositif de détermination, système de détermination, programme de détermination et support d'enregistrement Pending WO2025089029A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2023-182778 2023-10-24
JP2023182778 2023-10-24

Publications (1)

Publication Number Publication Date
WO2025089029A1 true WO2025089029A1 (fr) 2025-05-01

Family

ID=95515173

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2024/035849 Pending WO2025089029A1 (fr) 2023-10-24 2024-10-07 Procédé de détermination, dispositif de détermination, système de détermination, programme de détermination et support d'enregistrement

Country Status (1)

Country Link
WO (1) WO2025089029A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008500837A (ja) * 2004-05-28 2008-01-17 アンビオン インコーポレーティッド マイクロｒｎａに関与する方法および組成物
JP2018099031A (ja) * 2015-03-20 2018-06-28 アンジェス株式会社 ｃ−Ｍｅｔ陽性癌の判定方法
WO2021132547A1 (fr) * 2019-12-25 2021-07-01 東レ株式会社 Procédé de test, dispositif de test, procédé d'apprentissage, dispositif d'apprentissage, programme de test et programme d'apprentissage

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008500837A (ja) * 2004-05-28 2008-01-17 アンビオン インコーポレーティッド マイクロｒｎａに関与する方法および組成物
JP2018099031A (ja) * 2015-03-20 2018-06-28 アンジェス株式会社 ｃ−Ｍｅｔ陽性癌の判定方法
WO2021132547A1 (fr) * 2019-12-25 2021-07-01 東レ株式会社 Procédé de test, dispositif de test, procédé d'apprentissage, dispositif d'apprentissage, programme de test et programme d'apprentissage

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YUNKE SONG, KELVIN J. LIU, TZA-HUEI WANG: "Elimination of Ligation Dependent Artifacts in T4 RNA Ligase to Achieve High Efficiency and Low Bias MicroRNA Capture", PLOS ONE, vol. 9, no. 4, pages e94619, XP055271347, DOI: 10.1371/journal.pone.0094619 *

Similar Documents

Publication Publication Date Title
Yeri et al. Evaluation of commercially available small RNASeq library preparation kits using low input RNA
CN113053535B (zh) 一种医疗信息预测系统及医疗信息预测方法
JP7661878B2 (ja) 検査方法、検査装置、学習方法、学習装置、検査プログラムおよび学習プログラム
EP3304093A1 (fr) Validation de mesure de biomarqueurs
JP6208227B2 (ja) バイオマーカシグネチャを生成するためのシステムおよび方法
CN113260710A (zh) 用于通过多个定制掺合混合物验证微生物组序列处理和差异丰度分析的组合物、系统、设备和方法
WO2014050160A1 (fr) Dispositif de détection d'un biomarqueur de réseau dynamique, procédé de détection et programme de détection
CN113862351A (zh) 体液样本中鉴定胞外rna生物标志物的试剂盒及方法
WO2025089029A1 (fr) Procédé de détermination, dispositif de détermination, système de détermination, programme de détermination et support d'enregistrement
CN110970089B (zh) 胎儿浓度计算的预处理方法、预处理装置及其应用
US20120245048A1 (en) Cellular response assay for biofluid biomarker discovery and detection
EP4211272A1 (fr) Biomarqueurs pour diagnostiquer une maladie telle qu'une maladie cardiaque ou cardiovasculaire
CN110931082A (zh) 一种用于基因检测评估的方法及系统
WO2025089033A1 (fr) Procédé de test, dispositif de test, système de test, programme de test, support d'enregistrement et procédé de génération d'un modèle entraîné au temps de traitement
WO2025089035A1 (fr) Procédé de test, dispositif de test, système de test, programme de test, support d'enregistrement et procédé de génération de modèle entraîné par condition de stockage
WO2025089028A1 (fr) Procédé d'évaluation, dispositif d'évaluation, système d'évaluation, programme d'évaluation, support d'enregistrement et procédé de génération de modèle entraîné pour degré d'hémolyse
CN105316223A (zh) 生物学样品分析系统及方法
WO2025089036A1 (fr) Procédé de production de modèle de détermination de propriété, modèle de détermination de propriété, procédé de détermination de propriété et dispositif de détermination de propriété
Sorbie et al. Increasing transparency and reproducibility in stroke-microbiota research: A toolbox for microbiota analysis
WO2025027987A1 (fr) Procédé de détection, dispositif de présentation, dispositif de détection, système de détection, programme de détection et support d'enregistrement
WO2025094604A1 (fr) Procédé de détection, dispositif de détection, système de détection, programme de détection et support d'enregistrement
WO2025089057A1 (fr) Procédé de préparation et procédé de stockage d'échantillon de sang pour une mesure de petit arn, procédé de mesure de niveau d'expression de petit arn, procédé d'évaluation de maladie, procédé d'évaluation, procédé de génération de modèle, dispositif d'évaluation, système d'évaluation, programme d'évaluation et support d'enregistrement
CN114359890A (zh) 一种基于卷积神经网络的病原微生物检测识别方法
CN118038961B (zh) 基于聚合酶链反应的样本分析处理装置及电子设备
CN115919273B (zh) 一种基于深度学习的亚健康预警系统及相关设备

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24882146

Country of ref document: EP

Kind code of ref document: A1