JP6777351B2 - Programs, information processing equipment and information processing methods - Google Patents

Description

The present invention relates to programs, information processing devices and information processing methods.

Pathological tests, genetic tests, etc. are performed using samples collected from patients by biopsy, blood sampling, surgery, etc. In genetic testing, a genome analyzer or the like that visualizes the base sequence of nucleic acid read by using a sequencer has been proposed (Patent Document 1).

International Publication No. 2016-175330

Information on the relationship between the mutation state of the base sequence and the effect of anticancer drugs and the prognosis of patients is updated from time to time by many researchers and public institutions. When deciding on a treatment policy, it is desirable to make a decision based on the latest information. On the other hand, when judging the quality of the past treatment policy, it is necessary to refer to the knowledge at the time when the treatment policy is decided.

However, the genome analyzer described in Patent Document 1 cannot output the relationship between the base sequence read from the sample and the current and past information.

The program obtains the analysis results for the specimen containing the detected mutation from biopsy material, when receiving a report output request, and gene mutation, and the acquired genetic alterations related to medical information from a plurality of sources, from the integrated DB that integrates in association with an acquisition date and the basis information of the medical information, extracts the medical information the acquired genetic variation as a key, and the analysis results on the specimen, and extracted medical information, of the integrated DB outputting a report recorded in association with the version, extracts medical information when receiving a report output request in a past date and the date, said from the integrated DB before Symbol date, the acquired the gene mutation as a key Then, a report was output in which the analysis result regarding the sample, the extracted medical information, and the version of the integrated DB were recorded in association with each other, and the integrated DB was updated by adding the medical information regarding the gene mutation. In the case, an additional report in which medical information is extracted from the updated integrated DB using the acquired gene mutation as a key, and the analysis result regarding the sample, the extracted medical information, and the version of the integrated DB are recorded in association with each other. Have the computer execute the process of outputting the book .

One aspect is to provide a program or the like that outputs the relationship between the base sequence read from the sample and the current and past information.

It is explanatory drawing explaining the flow of processing using a genome analysis system. It is explanatory drawing explaining the generation method of the learning model. It is explanatory drawing explaining the outline of the integrated DB. It is explanatory drawing explaining the outline of the genome data. It is explanatory drawing explaining the structure of the genome analysis system. It is explanatory drawing explaining the record layout of a teacher data DB. It is explanatory drawing explaining the record layout of the integrated DB. It is explanatory drawing explaining the record layout of a report DB. It is explanatory drawing explaining the learning model. It is explanatory drawing explaining the example of a report. It is explanatory drawing explaining the example of a comment column. It is explanatory drawing explaining the example of the non-synonymous cell mutation column. It is explanatory drawing explaining the example of a germline mutation column. It is explanatory drawing explaining the example of the analysis column. It is a flowchart explaining the process flow of a program. It is explanatory drawing explaining the example of RNA column. It is explanatory drawing explaining the record layout of the change history DB. It is explanatory drawing explaining the record layout of the report DB of Embodiment 3. It is a flowchart explaining the process flow of the program which outputs an additional report. It is explanatory drawing explaining the record layout of an expert DB. It is explanatory drawing explaining the example of the screen which selects the participant to the expert panel. It is explanatory drawing explaining the example of the screen which confirms the participation request to the expert panel. It is a flowchart explaining the process flow of the subroutine of modification acceptance of Embodiment 4. It is explanatory drawing explaining the example of the integrated DB review participation request screen. It is a flowchart explaining the process flow of the program which updates the integrated DB. It is a functional block diagram of an information processing apparatus at the stage of predicting a clinically meaningful gene mutation from genomic data. It is a functional block diagram of an information processing apparatus at the stage of preparing a report based on a gene mutation and an integrated DB. It is explanatory drawing explaining the structure of the genome analysis system of Embodiment 7.

[Embodiment 1]
FIG. 1 is an explanatory diagram illustrating a flow of processing using the genome analysis system 10. The genome means the entire genetic information of one individual, here one human.

Specimens are taken from the patient. It is desirable that the sample be collected from both the tumor part and the normal part, respectively. The sample of the tumor part is collected by biopsy or surgery of the lesion part. In the following description, a sample collected from the tumor site is referred to as a tumor sample. Except for patients with abnormal blood such as blood cancer, samples of normal parts are often collected by blood sampling or the like. In the case of a patient with blood cancer, a sample of the tumor part is collected from the blood, and a sample of the normal part is collected from other normal tissues.

Nucleic acid, that is, DNA (Deoxyribonucleic Acid) or RNA (Ribonucleic Acid) is extracted from each sample. In the following description, a case where DNA is extracted will be described as an example. The base sequence of DNA is read by the reading device 31, and genomic data is created. Details of the genomic data will be described later. In the following description, the reading device 31 will be described by taking the case of a next-generation sequencer as an example, but the reading device 31 may be a DNA microarray or any other device or device that reads a base sequence.

Genome data is input to the learning model 53. From the learning model 53, a prediction of a clinically meaningful gene mutation is output. A draft report is automatically created based on the output gene mutation and the integrated database (Database) 52 that integrates the information collected from the medical literature and the like. Details of the learning model 53 and the integrated DB 52 will be described later.

In addition, the prediction of the gene mutation may be output from the learning model 53 regardless of the presence or absence of clinical significance. In such a case, clinically meaningful mutations are extracted based on the gene mutations output from the learning model 53 and the integrated DB 52, and a draft report is automatically prepared.

The report is completed by an expert panel of experts such as oncologists and geneticists reviewing the draft report and modifying it as necessary. The clinician in charge of treating the patient looks at the report and decides the treatment policy. The draft report and the details of the report will be described later. The review by the expert panel does not have to be performed. In this case, the clinician looks at the draft report output from the integrated DB 52 and decides the treatment policy.

FIG. 2 is an explanatory diagram illustrating a method of generating the learning model 53. A pathological examination is performed using a sample of the tumor site. The part containing the tumor cells is cut out from the sample of the tumor part. The DNA of the tumor part is extracted from the cut sample. The DNA of the normal part is extracted from the sample of the normal part. The DNA of the normal part and the DNA of the tumor part are put into the reading device 31 to create genomic data.

Based on the results of pathological tests, genomic data, and other test values, experts on whether the tumor is benign or malignant, whether it is primary cancer, the tumor content in the tumor sample, and drugs that can be expected to be effective. Judges and creates diagnostic data.

The genomic data and the diagnostic data are associated and recorded in the teacher data DB 51 (see FIG. 5). The details of the teacher data DB 51 will be described later. Supervised machine learning is performed based on the teacher data DB 51, and a learning model 53 is generated. The learning model 53 is a learned model that outputs a prediction regarding a gene mutation in a sample when genomic data obtained by reading a base sequence contained in the sample is input.

FIG. 3 is an explanatory diagram illustrating an outline of the integrated DB 52. The integrated DB 52 is a DB that associates and integrates medical information related to gene mutations acquired from a plurality of information sources with the acquisition source of the medical information. Information sources include, for example, DBs that publish medical papers, DBs that publish information on clinical trials of drugs or treatments by the national government or research institutes, and public information such as medical press releases issued by companies or universities. It is various medical information DB 58 such as the said DB.

The medical information DB 58 may be a DB that is open to the public free of charge or a DB that is open to the public for a fee. When using a DB that is open to the public for a fee, license processing such as concluding an appropriate license agreement between the provider of the paid DB and the provider of the integrated DB 52 is performed.

Medical information is recorded in different formats in each medical information DB 58, and the information is updated at different timings. An integrated DB 52 is created by crawling to access each medical information DB 58, collect information, and create a database.

Crawling is performed as appropriate, and an updated integrated DB 52 is created. Each integrated DB 52 is version-controlled in a state where, for example, an update date or an update date and time can be determined. Details of the integrated DB 52 will be described later.

In addition, each integrated DB 52 may be configured so that a difference from the previous version or a difference from an arbitrary version is recorded, and the integrated DB 52 at an arbitrary time can be constructed as needed. By recording the difference, the recording capacity of the integrated DB 52 can be saved.

FIG. 4 is an explanatory diagram illustrating an outline of genomic data. Pretreatment is performed on the sample. Specifically, as described above, DNA is extracted from the sample. The extracted DNA is subjected to treatments such as purification, fragmentation and amplification. Fragmentation cuts the DNA into fragments of length suitable for reading by the reader 31 used in the subsequent step.

The reading device 31 sequentially reads the base sequence of each fragmented DNA. Information about the base sequence read from one DNA fragment is called a read. The read also records a quality score that indicates the reliability of the reading for each base.

Each read is mapped to a reference sequence such as the Japanese Reference Genome (JRG) or the International Human Genome Reference Sequence. The mapping result is recorded in a file of, for example, BAM format, SAM format or CRAM format.

Information about the difference between the mapping result and the reference sequence, that is, the position where the genome of the sample is mutated with respect to the reference sequence, the content of the mutation, and the like is recorded in a file in, for example, VCF format or BCF format.

The VCF format file contains many mutations of low clinical importance, such as intron mutations in which the genetic information is not encoded and synonymous mutations in which the encoded amino acids are not changed. Therefore, reading information for determining a treatment policy or the like from a VCF format file requires a high degree of specialized knowledge.

Given a FASTQ format file and a reference sequence, it can be converted into a BAM format, a SAM format, a CRAM format and a VCF format file by a known analysis method. The data in FASTQ format, BAM format, SAM format, CRAM format, VCF format and BCF format described above are collectively referred to as genomic data. The genomic data may be in any format other than the format exemplified here.

For example, the reading device 31 outputs a FASTQ format file, and an analysis device (not shown) converts the file into a BAM format file and a VCF format file. The reading device 31 may have a built-in analysis device and directly output BAM format and VCF format files. The information processing device 20 (see FIG. 5), which will be described later, may acquire a file in FASTQ format or BAM format and convert it to VCF format.

When performing CNA (Copy Number Alteration) analysis, the genomic data obtained from multiple normal part samples collected from patients is compared with the genomic data obtained from tumor part samples. To do.

A PON (Panel Of Normals) method may be used for CNA analysis. When PON is used, genomic data in, for example, BAM format or SAM format is created and stored for normal part samples collected from a plurality of people. The genomic data obtained from the tumor sample collected from the patient is compared with the preserved genomic data for analysis.

FIG. 5 is an explanatory diagram illustrating the configuration of the genome analysis system 10. The genome analysis system 10 includes an information processing device 20, a reading device 31, and a data server 32.

The information processing device 20 includes a control unit 21, a main storage device 22, an auxiliary storage device 23, a communication unit 24, and a bus. The control unit 21 is an arithmetic control device that executes the program of the present embodiment. The control unit 21 is composed of one or a plurality of CPUs (Central Processing Units), a multi-core CPU, a GPU (Graphics Processing Unit), and the like. The control unit 21 is connected to each hardware unit constituting the information processing device 20 via a bus.

The main storage device 22 is a storage device such as a SRAM (Static Random Access Memory), a DRAM (Dynamic Random Access Memory), and a flash memory. The main storage device 22 temporarily stores information necessary in the middle of processing performed by the control unit 21 and a program being executed by the control unit 21.

The auxiliary storage device 23 is a storage device such as a SRAM, a flash memory, or a hard disk. The auxiliary storage device 23 stores a teacher data DB 51, an integrated DB 52, a learning model 53, a report draft DB 55, a report DB 56, a program to be executed by the control unit 21, and various data necessary for executing the program. The teacher data DB 51, the integrated DB 52, the learning model 53, the report draft DB 55, and the report DB 56 may be stored in an external large-capacity storage device connected to the information processing device 20, a data server 32, or the like. ..

The communication unit 24 is an interface for communicating between the information processing device 20 and the network.

As described above, the reading device 31 is a next-generation sequencer, a DNA microarray, or any other device or device that reads a base sequence. The genomic data created based on the base sequence read by the reading device 31 is recorded in the data server 32. The control unit 21 can acquire the genomic data recorded in the data server 32 via the communication unit 24 and the network. The control unit 21 may acquire the genome data directly from the reading device 31 without going through the data server 32.

The information processing device 20 of the present embodiment is a general-purpose personal computer, a tablet, a large-scale computer, or a virtual machine that operates on the large-scale computer. The information processing device 20 may be composed of a plurality of personal computers, tablets, hardware such as a large computer, and the like. The information processing device 20 may be configured by a quantum computer. The information processing device 20 may be integrated with the reading device 31. The information processing device 20 may be realized by so-called cloud computing.

FIG. 6 is an explanatory diagram illustrating a record layout of the teacher data DB 51. The teacher data DB 51 is a DB that records the genomic data and the diagnostic data in association with each other. FIG. 6 shows one record of the teacher data DB 51.

The teacher data DB 51 has a sample field, a genome data field, and a diagnostic data field. The sample field includes a normal part sample field and a tumor part sample field. The genomic data field has a normal genomic field and a tumor genomic field. The teacher data DB 51 does not have to have a normal genome field.

Diagnostic data fields include non-synonymous cell mutation fields, germline mutation fields and tumor content fields. The non-synonymous cell mutation field has a gene field and a DNA mutation field. The germline mutation field has a gene field and a DNA mutation field. The teacher data DB 51 has one record for one set of teacher data. The diagnostic data field does not have to have a tumor content field.

In the normal part sample field, the part where the normal part sample was collected is recorded. In the tumor part sample field, the site where the tumor part sample was collected is recorded. In the normal part genome field, the file name of the genome data obtained from the normal part sample is recorded. In the tumor part genome field, the file name of the genome data obtained from the tumor part sample is recorded.

In the subfield of the non-synonymous cell mutation field, there are genes having non-synonymous cell mutations contained in the tumor genome, that is, somatic mutations that cause changes in amino acids encoded by the DNA base sequence, and mutation contents. Is recorded. Somatic mutations mean mutations that do not occur in the normal genome but do occur in the tumor genome. That is, non-synonymous cell mutations are mutations related to tumor characteristics.

For example, the first line of the non-synonymous cell mutation field in FIG. 6 shows that the 5164th base of the ARID1A (AT-rich interactive domain 1A) gene is mutated from C (cytosine) to T (thymine). .. Similarly, the second line shows that the 743rd base of the TP53 gene is mutated from G (guanine) to A (adenine).

In the subfield of the germline mutation field, a gene having a mutation contained in the normal genome and the content of the mutation are recorded. For example, the first line of the germline mutation field in FIG. 6 shows that the 1791 base of the BRAF gene is mutated from T to G.

In the non-synonymous cell field and germline mutation field, any number of gene mutations detected in the sample that need to be recorded in the teacher data are recorded.

In addition, instead of collecting a sample of a normal part and acquiring genomic data, a reference sequence such as a Japanese reference genomic sequence may be used. In this case, the results for germline mutations are putative results.

The diagnostic data field may have a synonymous cell mutation field that records the synonymous cell mutation. It may have a somatic mutation field instead of a non-synonymous cell mutation field and record both a somatic cell mutation and a non-synonymous cell mutation.

In the tumor content field, the tumor content of the sample collected from the tumor site is recorded. Tumor content is calculated, for example, based on the number of hetero SNPs (Single Nucleotide Polymorphisms). Tumor content may be calculated based on the allele frequency recorded in the BAM file or SAM file, or the allele frequency calculated from the data recorded in the BAM file or SAM file.

Tumor content may be calculated based on the ratio of the number of nucleated cells to the number of tumor cells observed by pathological examination, or the area occupied by the tumor cells in the microscopic field of view. The definition of tumor content is arbitrary, but it is desirable that a unified definition be used for all teacher data included in the teacher data DB 51.

FIG. 7 is an explanatory diagram illustrating the record layout of the integrated DB 52. The integrated DB 52 is a DB that associates and integrates medical information related to gene mutations acquired from a plurality of information sources with the acquisition source of the medical information. The integrated DB 52 has a version field, a genomic mutation field and a knowledge data field.

The version of the integrated DB 52 is recorded in the version field. In this embodiment, the integrated DB 52 is managed by the update date. The genomic mutation field has a sample field, a gene field, and a mutation content field. The knowledge data field has a carcinogenicity field, a clinical significance field, a corresponding drug field, a corresponding disease field, a level field and a rationale information field. The integrated DB 52 has one record for one medical piece of information about a gene mutation.

In the sample field, the site where the sample was collected is recorded. In the gene field, the gene in which the mutation is detected is recorded. In the record in which medical information regarding a combination of a plurality of mutations is recorded, a plurality of genes are recorded in the gene field.

In the mutation content field, the content of the mutation, such as a non-synonymous cell mutation or a germline mutation, is recorded. In addition, information on synonymous cell mutations in which the encoded amino acid does not change may also be recorded in the integrated DB 52.

In the carcinogenicity field, the carcinogenicity level of the genomic mutation is recorded. The clinical significance of genomic mutations is recorded in the clinical significance field. The knowledge data field may have only one of the carcinogenicity field and the clinical significance field.

In the corresponding drug field, drugs that are effective when administered to patients with genomic mutations are recorded. The drug under investigation may be recorded in the corresponding drug field. Diseases corresponding to genomic mutations are recorded in the corresponding disease field. The level field records the level of importance of the genomic mutation. In the rationale information field, information for accessing the rationale information such as a document, a database name, or an ID (Identifier) uniquely assigned to the information, which is the basis of the information described in the record, is recorded.

A "-" in each subfield of the knowledge data field means that there is no corresponding information.

FIG. 8 is an explanatory diagram illustrating the record layout of the report DB 56. The report DB 56 is a DB that records information about a sample in association with diagnostic data based on the sample. FIG. 8 shows one record of report DB56.

The report DB56 contains a sample ID field, a sample field, a genome data field, and an integrated DB Ver. It has fields, diagnostic data fields and expert ID fields. The sample field includes a normal part sample field and a tumor part sample field. The genomic data field has a normal genomic field and a tumor genomic field.

Diagnostic data fields include non-synonymous cell mutation fields, germline mutation fields and tumor content fields. The non-synonymous cell mutation field has a diagnostic data field and a knowledge data field. The diagnostic data field has a gene field and a DNA mutation field. The knowledge data field has a carcinogenicity field, a clinical significance field, a corresponding drug field, a corresponding disease field, a level field and a rationale information field.

The germline mutation field has a diagnostic data field and a knowledge data field. The diagnostic data field has a gene field and a DNA mutation field. The knowledge data field has a clinical significance field, a level field and a rationale information field. Report DB56 has one record for a set of samples.

In the sample ID field, a sample ID uniquely assigned to one set of samples is recorded. The sample ID is associated with the patient in cooperation with an electronic medical record system or the like. In the normal part sample field, the part where the normal part sample was collected is recorded. In the tumor part sample field, the site where the tumor part sample was collected is recorded. In the normal part genome field, the file name of the genome data obtained from the normal part sample is recorded. In the tumor part genome field, the file name of the genome data obtained from the tumor part sample is recorded. Integrated DB Ver. In the field, the version of the integrated DB 52 used when creating the report record is recorded.

In the subfield of the diagnostic data field in the non-synonymous cell mutation field, the gene having the non-synonymous cell mutation and the mutation content are recorded. Each subfield of the knowledge data field records medical information related to the gene mutation recorded in the diagnostic data field. Since the information recorded in each subfield is the same as the information recorded in the subfield of the same name in the integrated DB 52 described with reference to FIG. 7, the description thereof will be omitted.

In the subfield of the diagnostic data field in the germline mutation field, the gene having the germline mutation and the mutation content are recorded. Each subfield of the knowledge data field records medical information related to the gene mutation recorded in the diagnostic data field. Since the information recorded in each subfield is the same as the information recorded in the subfield of the same name in the integrated DB 52 described with reference to FIG. 7, the description thereof will be omitted.

In the expert ID field, an expert ID uniquely assigned to each expert who constitutes the expert panel that reviews the draft report automatically created by the control unit 21 by the program described later is recorded. One expert ID may be assigned to an expert group in which a plurality of experts participate.

Since the record layout of the draft report DB 55 is the same as the record layout of the report DB 56 described with reference to FIG. 8 except that it does not have an expert ID field, illustration and detailed description are omitted.

FIG. 9 is an explanatory diagram illustrating the learning model 53. The learning model 53 is a neural network including an input layer 531 and an intermediate layer 532 and an output layer 533. In FIG. 9, the case where the learning model 53 is CNN is illustrated. The convolutional layer and the pooling layer are not shown.

The inputs of the learning model 53 are the genome data of the tumor part, the genome data of the normal part, the part where the tumor part sample was collected, and the part where the normal part sample was collected. Genome data is, for example, a tensor of piled-up alignment information, and includes base sequence, strand information, base quality, map quality, and the like as components. The base sequence may be represented by the count of each base of A, T, G, and C. The data input to the learning model 53 is input to the input layer 531 via the repetition of the convolutional layer and the pooling layer (not shown).

The output of the learning model 53 is, for example, the probability of each item of the diagnostic data. Specifically, it is the probability that each clinically significant mutation has occurred and the probability that the tumor content is a predetermined value. For example, in FIG. 9, the top output node has a somatic mutation in which the 6952th base of the BRCA gene is mutated from C to T, and the second output node has a 6952th BRCA gene mutation. The probability of a germline mutation in which the base of is mutated from C to T is output.

Since the somatic cell contains an allele, the somatic cell of the sample has a "base 6952 of the BRCA gene" derived from the father and a "base 6952 of the BRCA gene" derived from the mother. Therefore, somatic cell mutations include mutations in both the father-derived gene and the mother-derived gene, mutations in only the father-derived gene, and mutations in only the mother-derived gene. Includes cases where

For example, the output of the training model 53 may be the scores of HomoRef, Hetero, and Homo Alt. HomoRef, Hetero, and HomoAlt are indicators used in variant callers for genome analysis such as deep variant.

The output node at the bottom of FIG. 9 outputs the probability that the tumor content is 10 percent. Output nodes include nodes that output the probability of any tumor content, eg, in 10 percent increments.

In the learning model 53, when genomic data and sampling sites are input to the input layer 531, it is probable that each clinically meaningful mutation occurs in the output layer 533 and that the tumor content is predetermined. Is output. In the learning stage, the control unit 21 uses the supervised data DB 51, which records the genomic data and the sample collection site in association with the diagnostic data regarding the presence or absence of clinically meaningful mutations and the tumor content, and back-propagates the error. Supervised machine learning is performed by calculating the parameters of the intermediate layer 532 using a method or the like.

Supervised machine learning can be performed by any method such as logistic regression, SVM (Support Vector Machine), random forest, CNN, RNN, or XGBost (eXtreme Gradient Boosting).

The learning model 53 may be generated using any computer. The generated learning model 53 is transmitted to the information processing device 20 via a network or the like and recorded in the auxiliary storage device 23. Semi-supervised learning may be used instead of supervised learning.

FIG. 10 is an explanatory diagram illustrating an example of Report 60. The report 60 is created by formatting the information recorded in the record of the report DB 56 and the information recorded in the electronic medical record into a format that is easy for the user to view. The report 60 includes a journal item column 61, a comment column 62, a non-synonymous cell mutation column 63, a germline mutation column 64, and an analysis column 65.

The bibliographic item column 61 includes an ID column 611, a patient information column 612, a sample column 613, a histopathological diagnosis column 614, and a sample number column 615. In the ID column 611, a patient ID uniquely assigned to the patient is displayed. In the patient information column 612, the gender and age of the patient are displayed. The patient information column 612 may not be displayed.

In the sample column 613, the normal part sample and the tumor part sample used for the genome analysis are displayed. In FIG. 10, “FFPE (Formalin Fixed Paraffin Embedded) lung” means that the lung tissue is embedded with formalin-fixed paraffin.

In the histopathological diagnosis column 614, findings by pathological diagnosis by observing the sample with a microscope are displayed. In the sample number column 615, a sample number uniquely assigned to the sample is displayed. The information displayed in the bibliographic item column 61 is acquired from the electronic medical record system using the sample ID of the report record described with reference to FIG. 8 as a key.

FIG. 11 is an explanatory diagram illustrating an example of the comment column 62. 11A to 11C show examples of comment fields 62 displayed in different reports. FIG. 11A shows comment section 62 of the report on "Pathologic" or germline mutations found to be pathogenic. It provides advice on the genes and locations of pathogenic germline mutations, their rationale, and future actions regarding germline mutations.

FIG. 11B shows an example of a report comment on a specimen having a low tumor content, i.e., a specimen that may have a problem with the quality of the tumor specimen. FIG. 11C shows an example of comments regarding a sample in which a carcinogenic mutation was found in a tumor part sample. Information about genes with somatic mutations associated with carcinogenesis and clinical trials associated with those genes is displayed.

The text displayed in the comment field 62 is created by combining fixed phrases by a known method based on the information recorded in the diagnostic field of the report DB 56. By selecting and displaying a fixed phrase related to a gene mutation that is highly pathogenic or carcinogenic among multiple gene mutations occurring in a sample, it is highly important even for clinicians with little knowledge of genetic testing. Information can be grasped quickly.

FIG. 12 is an explanatory diagram illustrating an example of a non-synonymous cell mutation column 63. FIG. 12 shows an example of a non-synonymous cell mutation column 63 displayed based on a non-synonymous cell mutation field in the report record exemplified in FIG.

The non-synonymous cell mutation column 63 includes a gene column 631, a cytoband column 632, a DNA mutation column 633, an amino acid mutation column 634, an allyl frequency column 635, and a knowledge data column 636. The information recorded in the non-synonymous cell mutation field is displayed in the gene column 631, the DNA mutation column 633, and the knowledge data column 636, respectively.

The position of the gene on the chromosome is displayed in the cytoband column 632. In the amino acid mutation column 634, mutations of amino acids caused by DNA mutations are displayed. In the allyl frequency column 635, for example, the allyl frequency recorded in the BAM file or the SAM file, or the allyl frequency calculated from the data recorded in the BAM file or the SAM file is displayed.

At the upper part of the non-synonymous cell mutation column 63, the total number of somatic cell mutations including somatic mutations not described in the non-synonymous cell mutation column 63 and the total cell mutation frequency are displayed. The number of total cell mutations and the frequency of total cell mutations can be obtained from a VCF format file.

FIG. 13 is an explanatory diagram illustrating an example of a germline mutation column 64. FIG. 13 shows an example of a germline mutation column 64 displayed based on a germline mutation field in the report record exemplified in FIG.

The germline mutation column 64 includes a gene column 641, a cytoband column 642, a DNA mutation column 643, an amino acid mutation column 644, a normal part allele frequency column 647, a tumor part allyl frequency column 648, and a knowledge data column 645. The information recorded in the germline mutation field is displayed in the gene column 641, the DNA mutation column 643, and the knowledge data column 645, respectively.

The position of the gene on the chromosome is displayed in the sight band column 642. In the amino acid mutation column 644, mutations of amino acids caused by DNA mutations are recorded. In the normal part allyl frequency column 647, for example, the allyl frequency of the normal part recorded in a file of BAM format or SAM format is displayed. In the tumor part allele frequency column 648, for example, the allele frequency of the tumor part recorded in a BAM format or SAM format file is displayed.

FIG. 14 is an explanatory diagram illustrating an example of the analysis column 65. The analysis column 65 includes an estimated tumor content column 651 and a mutation frequency correlation coefficient column 652. In the estimated tumor content column 651, the estimated tumor content based on the output of the learning model 53 is displayed.

In the mutation frequency correlation coefficient column 652, the correlation coefficient between the gene mutation frequency in the sample collected from the normal part and the gene mutation frequency in the sample collected from the tumor part is displayed. When the correlation coefficient is high, the same base is often mutated in the normal part and the abnormal part, and it is determined that the sample is derived from the same patient. If the correlation coefficient is lower than the threshold value, it is suspected that the sample is mistaken or contamination occurs.

The mutation frequency correlation coefficient column 652 may not be displayed. For example, when the analysis is performed without using the normal part sample, the mutation frequency correlation coefficient column 652 is unnecessary.

When the user selects each column described with reference to FIGS. 10 to 14 by, for example, right-clicking, the control unit 21 displays the information recorded in the basis information field of the report record. The control unit 21 may display a link to the rationale information based on the rationale information field, or may display the rationale information itself. The user can confirm the reliability of the report by viewing the basis of the description in the report 60.

In the report 60, the contact information of the expert panel that carried out the review may be displayed. The user can ask questions, consult, etc. to the expert panel based on the report 60.

The report may include information such as pretreatment performed on the sample, the number of reads read by the reading device 31 on the base sequence, or the mapping depth to the reference sequence. A clinician familiar with genetic testing can use this information to determine the reliability of a report.

FIG. 15 is a flowchart illustrating a process flow of the program. The control unit 21 acquires genomic data from the data server 32 based on the report creation request (step S501). The control unit 21 creates a new record in the draft report DB 55 and records the data in the sample ID field, the sample field, and the genome data field, respectively (step S502).

The control unit 21 inputs the acquired genomic data into the learning model 53 and acquires the prediction probability of each node of the output layer 533 (step S503). The control unit 21 extracts a gene mutation for which a probability equal to or higher than a predetermined threshold is output from a node involved in the gene mutation in the output layer 533 (step S504). The threshold value may be a different value for each gene mutation or a constant value.

The control unit 21 determines the tumor content in the sample based on the node having the highest probability among the nodes related to the tumor content of the output layer 533 (step S505). The control unit 21 puts the mutation extracted in step S504 in the diagnostic data field of the non-synonymous cell mutation field or germline mutation field of the draft report record prepared in step S502, and determines the tumor in the tumor content field in step S505. Each content is recorded (step S506).

The tumor content may be calculated by an independent program different from the program shown in FIG. In that case, step S505 is unnecessary.

The control unit 21 searches the integrated DB 52 using the sample collection site and the gene mutation recorded in the draft report record as keys, and acquires knowledge data from the knowledge data field of the extracted record (step S507). The control unit 21 records the acquired knowledge data in the report record (step S508).

The control unit 21 determines whether or not the processing of all the gene mutations recorded in the draft report record has been completed (step S509). If it is determined that the process has not been completed (NO in step S509), the control unit 21 returns to step S507. When it is determined that the report has been completed (YES in step S509), the control unit 21 creates a draft report 60 described with reference to FIG. 10 based on the report record, and records it in the auxiliary storage device 23 or the data server 32. (Step S510).

Experts who are members of the Expert Panel will review the draft Report 60 at regular or irregular expert meetings and revise it as necessary. The expert meeting may be held by the experts actually gathering in one room, or may be held by a video conference, a telephone conference, or the like. The expert meeting may be held as an electronic meeting using a chat system or the like.

The expert panel refers to genomic data such as FASTQ format, BAM format, and VCF format as needed. The expert panel may refer to a micrograph or the like taken at the time of pathological examination. The expert panel may collect information from the pathologist in charge of the pathological examination or the clinician in charge of the patient.

The control unit 21 receives the correction decided at the expert meeting (step S511). The control unit 21 records in the report DB 56 a report record in which the information recorded in the draft report record is modified (step S512). The control unit 21 records an expert ID uniquely assigned to the expert who performed the review in the expert ID field of the report record. The control unit 21 ends the process.

The control unit 21 may notify the clinician that the report has been prepared by e-mail or any other means. The control unit 21 may upload the report to the electronic medical record system. The control unit may notify that there is a new report when the clinician logs in to the genome analysis system 10.

The control unit 21 may accept the designation of the date of the integrated DB 52 that creates the report 60 at the start of the program described with reference to FIG. When the date designation is accepted, the control unit 21 acquires the knowledge data by using the latest integrated DB 52 on the date designated in step S507. In step S510, the control unit 21 records a draft report based on the latest information on the designated date.

For example, when verifying the validity of a treatment policy determined in the past, the latest information on that date can be obtained by specifying the date on which the medical practice was performed and executing the program described using FIG. Can prepare a draft report based on.

Data may be added to the teacher data DB 51 based on the information recorded in the report DB 56, the post-treatment information, the post-medication information, and the like to relearn the learning model 53. The accuracy of the learning model 53 can be improved by adding the data reviewed by the experts to the teacher data.

According to this embodiment, it is possible to provide a learning model 53 that automatically extracts clinically important mutations based on the base sequence read from the sample. By using the learning model 53, even a doctor who does not have a high degree of expertise in genetic testing can determine the presence or absence of a clinically important gene mutation.

According to this embodiment, it is possible to provide a genome analysis system 10 that presents medical information on gene mutations to a user by using the integrated DB 52. In the field of genetic testing, research speed is fast and new findings are frequently announced, so it is difficult for individual doctors to keep up to date with the latest information. Since medical information is provided and the rationale is also presented based on the integrated DB 52, the doctor can confirm the rationale as necessary and provide appropriate medical care to the patient.

By reviewing the draft report on the expert panel and reflecting the corrections made by the expert panel, it is possible to provide the genome analysis system 10 for producing a highly reliable report 60. By conducting a review by the expert panel, the report 60 can be created based on new information not included in the teacher data DB 51.

If the clinician has expertise in genetic testing, the draft report may be used as is for Report 60, omitting the review by the expert panel. The patient or clinician may obtain the draft report and genomic data and seek the opinion of a specialist of his choice.

[Embodiment 2]
The present embodiment relates to a genome analysis system 10 that analyzes the base sequence of RNA in addition to DNA. The description of the parts common to the first embodiment will be omitted.

In the present embodiment, the sample collected from the tumor portion is divided into three. One is used for pathological examination and one is used for DNA analysis. In the last one, RNA is extracted by pretreatment, the base sequence of RNA is read by the reading device 31, and the RNA is analyzed by the same method as DNA.

By analyzing RNA, information on genetic abnormalities expressed in tumors can be obtained. The gene abnormality expressed in the tumor site is, for example, a fusion gene in which a plurality of DNAs are fused by translocation or gene rearrangement, or exon skipping in which a part of the DNA is dropped when it is transcribed into RNA. In the report 60 of this embodiment, for example, an RNA column 66 displaying information obtained by analyzing RNA is displayed between the non-synonymous cell mutation column 63 and the germline mutation column 64.

FIG. 16 is an explanatory diagram illustrating an example of RNA column 66. 16A and 16B show examples of RNA column 66 displayed in different reports. FIG. 16A shows an example of RNA column 66 for a sample in which no abnormality is found in RNA. FIG. 16B shows an example of RNA column 66 for a sample in which a fusion gene and exon skipping were found.

The RNA column 66 shown in FIG. 16B includes a gene column 661, a mutation column 667, a sight band column 662, a read number column 668, and a knowledge data column 666. In the gene column 661, the gene from which the RNA was transcribed is displayed.

Mutations in RNA are displayed in the mutation column 667. For example, the top row of FIG. 16B shows that a fusion gene of the PAX3 gene and the FOXO1 gene was detected. The bottom row of FIG. 16B shows that exon 1 skipping of the MET gene was detected.

The position of the gene on the chromosome is displayed in the sight band column 662. In the read number column 668, the number and ratio of the reads in which the mutation is detected among the reads read by the reading device 31 are displayed. The information displayed in the read number column 668 is read from the FASTQ format file. The information acquired from the integrated DB 52 is displayed in the knowledge data column 666.

According to this embodiment, it is possible to provide a genome analysis system 10 that detects an abnormality in a gene expressed in a tumor and displays it in Report 60.

[Embodiment 3]
The present embodiment relates to a genome analysis system 10 that outputs an additional report indicating changes in the previously output report 60 when the integrated DB 52 is updated. The description of the parts common to the first embodiment will be omitted.

FIG. 17 is an explanatory diagram illustrating a record layout of the change history DB. The change history DB is a DB that records the gene mutation recorded in the integrated DB 52 in association with the change date in which the knowledge data is changed. The change history DB has a genome mutation field and a change date field.

The genome mutation field has a tumor part sample field, a gene field, and a mutation content field. The change date field has an arbitrary number of subfields such as a first change date field and a second change date field. The change history DB has one record for one medical information recorded in the integrated DB 52.

In the tumor part sample field, the site where the sample was collected is recorded. In the gene field, the gene in which the mutation is detected is recorded. In the record in which medical information regarding a combination of a plurality of mutations is recorded, a plurality of genes are recorded in the gene field.

In the first modification date field, the date on which the record relating to the gene mutation recorded in the genome mutation field is recorded in the integrated DB 52 is recorded. After the second modification date field, the date on which the medical information recorded in the integrated DB 52 was modified is recorded.

FIG. 18 is an explanatory diagram illustrating a record layout of the report DB 56 of the third embodiment. The report DB 56 of the present embodiment has a confirmation date field added to the report DB 56 of the first embodiment described with reference to FIG. In the confirmation date field, the date on which the update status of the integrated DB 52 is confirmed is recorded.

FIG. 19 is a flowchart illustrating a processing flow of a program that outputs an additional report. The control unit 21 acquires the report record recorded in the report DB 56 (step S521). The control unit 21 acquires the site from which the sample was collected recorded in the normal part sample field and the tumor part sample field (step S522). The control unit 21 acquires the confirmation date recorded in the confirmation date field (step S523).

The control unit 21 acquires the gene mutation recorded in the gene field of the non-synonymous cell mutation field or the germline mutation field (step S524). The control unit 21 searches the change history DB using the site where the sample acquired in step S522 was collected and the gene mutation acquired in step S524 as a key, and extracts a record. The control unit 21 compares the date recorded in the change date field of the extracted record with the confirmation date acquired in step S523, and determines whether or not the knowledge data has been changed after the confirmation date (step S525).

When it is determined that the knowledge data has not been changed (NO in step S525), the control unit 21 returns to step S524. When it is determined that the knowledge data has been changed (YES in step S525), the control unit 21 searches the latest integrated DB 52 using the site where the sample acquired in step S522 was collected and the gene mutation acquired in step S524 as keys. To extract records. The control unit 21 acquires knowledge data from the extracted records (step S526).

The control unit 21 records the knowledge data acquired in step S526 in the knowledge data field of the report record (step S527). The control unit 21 may make a copy of the report record and record the knowledge data acquired in step S526.

The control unit 21 determines whether or not the processing of all the mutations recorded in the report record acquired in step S521 has been completed (step S528). If it is determined that the process has not been completed (NO in step S528), the control unit 21 returns to step S524.

When it is determined that the process is completed (YES in step S528), the control unit 21 determines whether or not there is a gene mutation determined in step S525 that the knowledge data has been changed (step S529). If it is determined to be present (YES in step S529), the control unit 21 notifies the clinician that the report has been changed (step S530). Notifications can be made by any means, such as email or messenger.

The control unit 21 may notify the clinician or the hospital after notifying the expert panel in step S530 and accepting the correction based on the review result. When it is determined that there is no gene mutation determined that the knowledge data has been changed (NO in step S529) or after the end of step S530, the control unit 21 determines whether or not to end the process (step S531).

If it is determined that the process is not completed (NO in step S531), the control unit 21 returns to step S521. If it is determined to end (YES in step S531), the control unit 21 ends the process.

According to this embodiment, it is possible to provide a genome analysis system 10 that outputs an additional report when new medical information related to a report prepared in the past is released. The clinician can receive additional information about drugs, clinical trials, treatments, etc. that are expected to be effective for the patient being treated and reflect them in the treatment policy.

The control unit 21 may accept the designation of the report 60 that does not require additional information. The clinician can specify that no additional report is required for reports 60 and the like regarding patients who have completed treatment. In step S521, the control unit 21 excludes reports that do not require additional information from the acquisition target, thereby avoiding the creation of unnecessary additional reports.

[Embodiment 4]
The present embodiment relates to a genome analysis system 10 that provides incentives to experts who participate in the expert panel. The description of the parts common to the first embodiment will be omitted.

FIG. 20 is an explanatory diagram illustrating a record layout of the expert DB. The expert DB is a DB that records an expert ID uniquely assigned to an expert participating in the expert panel, a specialized field, and points in association with each other.

The expert DB has an expert ID field, a specialty field, and a point field. The expert ID is recorded in the expert ID field. In the field of specialization, the field of specialization of the specialist is recorded. In the point field, the points given to the expert are recorded.

Experts can earn points each time they participate in an expert panel and review a draft report. Experts can use the accumulated points, for example, a gold ticket, a report creation request ticket that can be used when requesting the creation of report 60, or a learning model usage ticket that can be used when requesting gene analysis using the learning model 53. Can be exchanged for etc. Points can give professionals an incentive to participate in the expert panel.

The points may be set as, for example, 5 points in one review. Based on the amount of remarks or the content of opinions at the time of expert review, for example, the leader of the expert panel may determine the points to be given to individual experts. The points to be awarded for one review may be determined based on the frequency of participation in the expert panel.

FIG. 21 is an explanatory diagram illustrating an example of a screen for selecting a participant to the expert panel. The screen shown in FIG. 21 is displayed on an information device such as a personal computer, tablet, or smartphone used by the secretariat staff of the expert panel. The information device used by the secretariat staff is connected to the information processing device 20 via a network.

The screen for selecting participants in the expert panel includes a sample information field 74, a narrowing condition field 75, a re-search button 76, a candidate list 77, a confirmation button 78, and a request transmission button 79. In the sample information column 74, information regarding a sample to be reviewed by the expert panel is displayed.

In the narrowing condition column 75, items to be used when narrowing down experts are displayed. The user can select the narrowing conditions by selecting the check box displayed at the beginning of each item. The narrowing-down condition column 75 may have a column for accepting free keywords. In the candidate list 77, a candidate list of experts participating in the expert panel is displayed.

The user sets a desired condition using the narrowing condition field 75 and selects the search button 76 again. The set conditions are transmitted to the information processing device 20. The control unit 21 extracts an expert who meets the set conditions and transmits the expert to the information device used by the user.

The candidate list 77 displays a list of experts who meet the set conditions. The user uses the check box displayed at the right end of the candidate list 77 to select an expert to request participation in the expert panel.

If the number of experts displayed in the candidate list 77 is too large or too small, the user appropriately changes the setting of the narrowing condition field 75 and performs a re-search. If the user selects the confirmation button 78, a list of selected experts is displayed. When the user selects the request transmission button 79, a list of selected experts is transmitted to the information processing device 20.

The control unit 21 associates the sample ID with the expert ID of the selected expert and stores it in the auxiliary storage device 23. The control unit 21 sends an e-mail containing a URL (Uniform Resource Locator) to each expert.

FIG. 22 is an explanatory diagram illustrating an example of a screen for confirming a request for participation in the expert panel. FIG. 22 is a screen displayed on the information device used by the expert when the expert accesses the WEB site indicated by the URL.

The screen for confirming the participation request to the expert panel includes the request list 72 and the participation button 71. In the request list 72, a list of expert panels for asking experts to participate is displayed. For each expert panel, information such as a sample collection site, patient information, and a medical institution that requested the preparation of report 60 is displayed.

The expert looks at the request list 72 and selects the join button 71 for the expert panel he wishes to join. The control unit 21 sets up an electronic conference room in which an expert who has selected the participation button 71 participates, and uploads a draft report. Participants will review the report in the electronic conference room. A pre-designated leader summarizes the conclusions and terminates the electronic conference room. Since the electronic conferencing system has been widely used in the past, the details of the processing performed by the control unit 21 will be omitted.

After the end of the electronic conference room, the control unit 21 gives points to the experts who participated in the expert panel. Specifically, the control unit 21 extracts the record related to the expert who participated in the expert panel from the expert DB, and adds points to the point field.

FIG. 23 is a flowchart illustrating a processing flow of the subroutine of the modification reception of the fourth embodiment. The modification reception subroutine is a subroutine that accepts the participation of experts in the expert panel and gives points to the participating experts. The modification reception subroutine is activated instead of step S511 of the program of the first embodiment described with reference to FIG.

The control unit 21 creates an expert panel participation request screen described using FIG. 22 for each expert registered in the expert DB, sends an e-mail containing the URL, and notifies the participation request (step). S541).

The control unit 21 can determine which expert is requested to review which report draft based on the specialized field recorded in the specialized field of the expert DB. For example, the control unit 21 asks an expert whose respiratory organs are registered in the specialized field to participate in the expert panel regarding the cases in which the tumor part sample is collected from the respiratory organs and the cases requested by the respiratory department. Notice.

The control unit 21 may select an expert registered in the expert DB for each category and notify the participation request. The control unit 21 may notify all the experts registered in the expert DB of the participation request. The control unit 21 accepts participation in the expert panel by accepting the selection of the participation button 71 by an expert (step S542). The control unit 21 sets up an electronic conference room in which participants to each expert panel are registered (step S543). The control unit 21 transmits access information to the electronic conference room to each participant.

The control unit 21 uploads the draft report to the electronic conference room so that the participants can view it (step S544). Participants will communicate with other participants through the electronic conference room and review the draft report.

A pre-designated leader summarizes the conclusions and performs the operation of terminating the electronic conference room. The control unit 21 accepts the end operation (step S545). The control unit 21 closes the electronic conference room (step S546). The control unit 21 extracts the record related to the expert who participated in the expert panel from the expert DB, and adds points to the point field (step S547). The control unit 21 ends the process.

According to this embodiment, it is possible to provide a genome analysis system 10 that provides an incentive for participation in an expert panel. By distributing the profits obtained from the learning model usage fee, the report preparation fee, and the like to the experts by points, it is possible to provide the genome analysis system 10 that makes it easy to secure the experts to participate in the expert panel.

Since the experts themselves can decide whether or not to participate in each expert panel, it is possible to provide a genome analysis system 10 that can attract motivated participants. Since expert reviews are conducted using an electronic conference room, it is possible to provide a genome analysis system 10 that makes it easy for even busy experts to participate in the expert panel.

[Embodiment 5]
The present embodiment relates to a genome analysis system 10 that asks an expert to review the information recorded in the integrated DB 52. The description of the parts common to the fourth embodiment will be omitted.

FIG. 24 is an explanatory diagram illustrating an example of an integrated DB review participation request screen. The control unit 21 sends an e-mail containing a URL to each expert. When an expert accesses the WEB site indicated by the URL using an information device such as a personal computer or a smartphone, the integrated DB review participation request screen shown in FIG. 24 is displayed on the information device.

The integrated DB review participation request screen includes a request list 73 and a participation button 71. The request list 73 displays a list of medical information for which an expert is requested to review. For each medical information, the gene, DNA mutation and source of interest are displayed. The target of the integrated DB review is No. 24 in FIG. As illustrated in 3, the information may not be related to a specific gene mutation.

The expert can look at the request list 73 to determine if it is medical information about a drug, disease or clinical trial in his area of expertise. The expert selects the join button 71 if he wishes to participate in the review. The control unit 21 sets up an electronic conference room in which an expert who has selected the participation button 71 participates, and uploads a draft report. Participants will review the report in the electronic conference room. A pre-designated leader summarizes the conclusions and terminates the electronic conference room.

The review may be conducted independently by one expert. In that case, it is not necessary to use the electronic conference room.

The control unit 21 adds a new record to the integrated DB 52 or updates an existing record based on the review result.

FIG. 25 is a flowchart illustrating a processing flow of a program for updating the integrated DB 52. In the following description, a case where the information processing apparatus 20 updates the integrated DB 52 will be described as an example. The update of the integrated DB 52 may be executed by an information device other than the information processing device 20.

The control unit 21 patrols various medical information DB 58s, collects new medical information related to gene mutations, and creates a database for crawling (step S551). Crawling is performed by a program called a crawler or robot. Since crawling has been widely used in the past, detailed description thereof will be omitted.

The control unit 21 selects the medical information collected by crawling and determines whether or not the information is related to the gene mutation already recorded in the integrated DB 52 (step S552). When it is determined that the information is related to the gene mutation recorded in the integrated DB 52 (YES in step S552), the control unit 21 determines whether or not the information is the same as the information recorded in the integrated DB 52 (step). S553).

When it is determined that the information is not related to the gene mutation recorded in the integrated DB 52 (NO in step S552), or when it is determined that the information is not the same as the information recorded in the integrated DB 52 (NO in step S553). ), The control unit 21 records that the medical information being processed is the subject of the review (step S554).

If it is determined that the contents are the same (YES in step S553), or after the end of step S554, the control unit 21 determines whether or not the processing of the medical information collected in step S551 is completed (step S555). If it is determined that the process has not been completed (NO in step S555), the control unit 21 returns to step S552.

When it is determined that the process has been completed (YES in step S555), the control unit 21 creates an integrated DB review participation request screen described using FIG. 24 for each expert registered in the expert DB, and describes the URL. Is sent to notify the participation request (step S561).

The control unit 21 accepts participation in the review by accepting the selection of the participation button 71 by an expert (step S562). The control unit 21 sets up an electronic conference room in which participants for each review are registered (step S563). The control unit 21 transmits access information to the electronic conference room to each participant.

The control unit 21 uploads the medical information collected by crawling to the electronic conference room so that the participants can view it (step S564). Participants communicate with other participants through the electronic conference room and review medical information.

A pre-designated leader summarizes the conclusions and performs the operation of terminating the electronic conference room. The conclusion may be decided by a majority vote of the participating experts. The control unit 21 accepts the end operation (step S565). The control unit 21 closes the electronic conference room (step S566). The control unit 21 extracts the record related to the expert who participated in the review from the expert DB, and adds points to the point field (step S567). The control unit 21 updates the integrated DB 52 based on the review result regarding each medical information (step S568). The control unit 21 ends the process.

According to this embodiment, it is possible to provide a genome analysis system 10 that automatically collects information registered in the integrated DB 52 by crawling and then updates the integrated DB 52 after a review by an expert. By utilizing the crawling technique, it is possible to provide a genome analysis system 10 that appropriately reflects new medical information in the integrated DB 52.

By conducting a review by an expert before registering the collected medical information in the integrated DB 52, it is possible to provide a genome analysis system 10 that maintains the reliability of the integrated DB 52 and outputs an accurate report 60.

By distributing the profits obtained from the learning model usage fee, the report preparation fee, etc. to the experts by points, it is possible to provide the genome analysis system 10 that makes it easy to secure the experts to participate in the review.

Since the expert himself can decide whether or not to participate in each review, it is possible to provide a genome analysis system 10 that can attract motivated review participants. Since the review is performed using the electronic conference room, it is possible to provide the genome analysis system 10 that makes it easy for even a busy expert to participate in the review.

[Embodiment 6]
FIG. 26 is a functional block diagram of the information processing apparatus 20 at the stage of predicting a clinically meaningful gene mutation from genomic data. The information processing device 20 includes a genome data acquisition unit 81, a genome data input unit 82, and an output unit 83.

The genome data acquisition unit 81 acquires the genome data obtained by reading the base sequence contained in the sample. The genome data input unit 82 inputs the genome data acquired by the genome data acquisition unit 81 into the learning model 53 that receives the genome data and outputs the prediction regarding the gene mutation. The output unit 83 outputs the prediction output from the learning model 53 based on the genome data input by the genome data input unit 82.

FIG. 27 is a functional block diagram of the information processing apparatus 20 at the stage of creating a report based on the gene mutation and the integrated DB 52. The information processing device 20 has a first reception unit 84, a first output unit 85, a second reception unit 86, and a second output unit 87.

The first reception unit 84 receives the gene mutation detected in the sample. The first output unit 85 is based on the gene mutation received by the first reception unit 84 and the integrated DB 52 that integrates medical information regarding the gene mutation acquired from a plurality of information sources, the acquisition date of the medical information, and the basis information. Then, the report that records the analysis result of the sample and the version of the integrated DB 52 in association with each other is output.

The second reception unit 86 receives a past date, a report output request on that date, and a gene mutation detected in a sample. The second output unit 87 outputs a report in which the analysis result regarding the sample and the version of the integrated DB 52 are recorded in association with each other based on the gene mutation received by the second reception unit 86 and the integrated DB 52 on the date. ..

[Embodiment 7]
The present embodiment relates to a mode in which the genome analysis system 10 of the present embodiment is realized by operating a general-purpose computer 90 and a program 97 in combination. FIG. 28 is an explanatory diagram illustrating the configuration of the genome analysis system 10 of the seventh embodiment. The description of the parts common to the first embodiment will be omitted.

The genome analysis system 10 of the present embodiment includes a computer 90, a reading device 31, and a data server 32.

The computer 90 includes a control unit 21, a main storage device 22, an auxiliary storage device 23, a communication unit 24, a reading unit 29, and a bus. The computer 90 is an information device such as a general-purpose personal computer, a tablet, or a server computer.

The program 97 is recorded on the portable recording medium 96. The control unit 21 reads the program 97 via the reading unit 29 and stores it in the auxiliary storage device 23. Further, the control unit 21 may read the program 97 stored in the semiconductor memory 98 such as the flash memory mounted in the computer 90. Further, the control unit 21 may download the program 97 from the communication unit 24 and another server computer (not shown) connected via a network (not shown) and store the program 97 in the auxiliary storage device 23.

The program 97 is installed as a control program of the computer 90, loaded into the main storage device 22, and executed. As a result, the computer 90 functions as the information processing device 20 described above.

The technical features (constituent requirements) described in each embodiment can be combined with each other, and by combining them, a new technical feature can be formed.
The embodiments disclosed this time should be considered to be exemplary in all respects and not restrictive. The scope of the present invention is indicated by the scope of claims, not the above-mentioned meaning, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims.

10 Genome analysis system 20 Information processing device 21 Control unit 22 Main storage device 23 Auxiliary storage device 24 Communication unit 29 Reading unit 31 Reading device 32 Data server 51 Teacher data DB
52 Integrated DB
53 Learning model 531 Input layer 532 Intermediate layer 533 Output layer 55 Draft report DB
56 Report DB
58 Medical Information DB
60 Report 61 Journal matters column 611 ID column 612 Patient information column 613 Specimen column 614 Histopathological diagnosis column 615 Specimen number column 62 Comment column 63 Non-synonymous cell mutation column 631 Gene column 632 Site band column 633 DNA mutation column 634 Amino acid mutation Column 635 Allyl frequency column 636 Knowledge data column 64 Reproductive cell mutation column 641 Gene column 642 Site band column 643 DNA mutation column 644 Amino acid mutation column 645 Knowledge data column 647 Normal part Allyl frequency column 648 Tumor part Allyl frequency column 65 Analysis column 651 Estimated Tumor content column 652 Mutation frequency correlation coefficient column 66 RNA column 661 Gene column 662 Site band column 666 Knowledge data column 667 Mutation column 668 Read number column 71 Participation button 72 Request list 73 Request list 74 Specimen information column 75 Narrowing condition column 76 Re-search button 77 Candidate list 78 Confirmation button 79 Request send button 81 Genome data acquisition unit 82 Genome data input unit 83 Output unit 84 1st reception unit 85 1st output unit 86 2nd reception unit 87 2nd output unit 90 Computer 96 Portable recording medium 97 Program 98 Semiconductor memory

Claims (10)

Obtain the analysis result of the sample including the gene mutation detected in the sample,
When the report output request is accepted
Extraction and gene mutation, a medical information on the gene mutation acquired from multiple sources, from the integrated DB acquired date and integrated in association with the basis information of the medical information, medical information acquired the gene mutation as a key And
Output a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
When a report output request on a past date and that date is accepted
From the integrated DB in the date, extracts medical information acquired the gene mutation as a key,
Output a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
When the integrated DB is updated by adding medical information about gene mutation,
Medical information is extracted from the updated integrated DB using the acquired gene mutation as a key.
A program that causes a computer to execute a process of outputting an additional report that records an analysis result of the sample, extracted medical information, and a version of the integrated DB in association with each other. Obtain the analysis result of the sample including the genomic data obtained by reading the base sequence contained in the sample.
Input the acquired genomic data into a learning model that accepts genomic data and outputs predictions about gene mutations.
Based on the input genomic data, the prediction about the gene mutation output from the learning model is acquired, and the prediction is obtained.
Extraction and gene mutation, a medical information on the gene mutation acquired from multiple sources, before Symbol integrated DB that integrates in association with an acquisition date and the basis information of the medical information, medical information the acquired predicted as a key And
Output a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
When the integrated DB is updated by adding medical information about gene mutation,
Medical information is extracted from the updated integrated DB using the acquired prediction as a key.
A program that causes a computer to execute a process of outputting an additional report that records an analysis result of the sample, extracted medical information, and a version of the integrated DB in association with each other. Send a review request regarding the update of the integrated DB to an expert,
Accept the review results for the submitted review request,
Record incentives for accepted review results in association with the expert
The program according to claim 1 or 2 . Send a review request for the report to an expert
Accept the review results for the submitted review request,
The program according to any one of claims 1 to 3 , which records an incentive for the received review result in association with the expert. The incentive is a cash voucher, a report creation request voucher, or a learning model voucher.
The program according to claim 3 or 4 . The incentive varies based on the review results.
The program according to any one of claims 3 to 5 . The first reception section that receives the analysis results for the sample containing the gene mutation detected in the sample, and
When the report output request is accepted
A gene mutation in which the first receiving unit has received a medical information on the gene mutation acquired from multiple sources, from the integrated DB that integrates in association with an acquisition date and the basis information of the medical information, said gene obtained The first extraction unit that extracts medical information using mutation as a key,
A first output unit that outputs a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
The second reception department that accepts past dates and report output requests on that date,
From the integrated DB in the date, a second extractor for extracting medical information acquired the gene mutation as a key,
A second output unit that outputs a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other .
When the integrated DB is updated by adding medical information about gene mutation,
A third extraction unit that extracts medical information from the updated integrated DB using the acquired gene mutation as a key.
An information processing device including an additional output unit that outputs an additional report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other . A first reception unit that accepts analysis results related to the sample, including genomic data obtained by reading the base sequence contained in the sample.
A prediction acquisition unit that inputs the received genome data into the learning model that receives the genome data and outputs the prediction about the gene mutation, and acquires the prediction about the gene mutation output from the learning model based on the input genome data. ,
Medical information is extracted using the acquired prediction as a key from the integrated DB that integrates the gene mutation, the medical information related to the gene mutation acquired from a plurality of information sources, and the acquisition date and the basis information of the medical information. The first extraction unit and
A first output unit that outputs a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
When the integrated DB is updated by adding medical information about gene mutation,
A third extraction unit that extracts medical information from the updated integrated DB using the acquired prediction as a key.
An additional output unit that outputs an additional report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
Information processing device equipped with. Obtain the analysis result of the sample including the gene mutation detected in the sample,
When the report output request is accepted
Extraction and gene mutation, a medical information on the gene mutation acquired from multiple sources, from the integrated DB acquired date and integrated in association with the basis information of the medical information, medical information acquired the gene mutation as a key And
Output a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
When a report output request on a past date and that date is accepted
From the integrated DB in the date, extracts medical information acquired the gene mutation as a key,
Output a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
When the integrated DB is updated by adding medical information about gene mutation,
Medical information is extracted from the updated integrated DB using the acquired gene mutation as a key.
An information processing method in which a computer executes a process of outputting an additional report recorded by associating an analysis result of the sample with the extracted medical information and a version of the integrated DB . Obtain the analysis result of the sample including the genomic data obtained by reading the base sequence contained in the sample.
Input the acquired genomic data into a learning model that accepts genomic data and outputs predictions about gene mutations.
Based on the input genomic data, the prediction about the gene mutation output from the learning model is acquired, and the prediction is obtained.
Medical information is extracted using the acquired prediction as a key from the integrated DB that integrates the gene mutation, the medical information related to the gene mutation acquired from a plurality of information sources, and the acquisition date and the basis information of the medical information. ,
Output a report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
When the integrated DB is updated by adding medical information about gene mutation,
Medical information is extracted from the updated integrated DB using the acquired prediction as a key.
Output an additional report that records the analysis result of the sample, the extracted medical information, and the version of the integrated DB in association with each other.
An information processing method that causes a computer to perform processing.

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