WO2022085399A1

WO2022085399A1 - Therapeutic strategy drafting assistance device

Info

Publication number: WO2022085399A1
Application number: PCT/JP2021/036565
Authority: WO
Inventors: 祥歩阿部; 知弘安田
Original assignee: 株式会社日立製作所
Priority date: 2020-10-23
Filing date: 2021-10-04
Publication date: 2022-04-28
Also published as: JP2022069002A; JP7565186B2

Abstract

Provided is a therapeutic strategy drafting assistance device capable of assisting the drafting of a therapeutic strategy suitable for each patient. The therapeutic strategy drafting assistance device displays data for assisting the drafting of a therapeutic strategy, and is characterized by comprising: a data acquiring unit for acquiring gene mutation data or gene expression level data obtained by analyzing a cell of the site of lesion of a patient; a storage unit for storing pathway data comprising data of a pathway representing a link of genes by a directed graph; and a score calculating unit for calculating, on the basis of the gene mutation data or the gene expression level data and the pathway data, a gene score indicating the relevance between a disease and a gene.

Description

Treatment policy planning support device

The present invention relates to a treatment policy planning support device that supports the formulation of a treatment policy suitable for an individual patient.

Cancer genomic medicine that selects drugs that are expected to be effective against gene mutations that affect the disease will be launched in Japan in 2019, as opposed to conventional cancer treatments that select drugs based on the organs and pathological conditions in which cancer exists. It became covered by insurance. In cancer genome therapy, drugs are selected based on genetic information while referring to the pathway, which is a gene interaction network, as needed, but it requires specialized knowledge to interpret data such as gene mutations and is enormous. It takes time to handle the pathway, which is a mutual network.

Patent Document 1 discloses a molecular network analysis support device that efficiently obtains a biologically meaningful route from a pathway that forms a wide and complicated network. Specifically, based on known DBs such as medical literature DB (DataBase), interaction DB, and pharmacological DB, the strength of association with the designated in vivo phenomenon is calculated for each search route in the pathway, and the calculation result is obtained. It is disclosed that the search route related to the designated in vivo phenomenon can be selected by sorting.

International Publication No. 2008/102658

However, Patent Document 1 merely selects a search route related to a designated in vivo phenomenon such as a disease based on known data, and does not reflect the state of the gene in the patient's body. That is, it has not been possible to formulate a treatment policy suitable for each individual patient.

Therefore, an object of the present invention is to provide a treatment policy planning support device capable of supporting the formulation of a treatment policy suitable for an individual patient.

In order to achieve the above object, the present invention is a treatment policy planning support device that displays data that support the planning of a treatment policy, and gene mutation data or gene expression obtained by analyzing cells at a lesion site of a patient. The data acquisition unit for acquiring the amount data, the storage unit for storing the pathway data which is the pathway data representing the sequence between genes in a directed graph, the gene mutation data or the gene expression level data, and the pathway data. Based on this, the present invention provides an apparatus including a score calculation unit for calculating a gene score indicating the degree of association between a disease and a gene.

According to the present invention, it is possible to provide a treatment policy planning support device capable of supporting the formulation of a treatment policy suitable for an individual patient.

It is a hardware block diagram of the treatment policy planning support device of Example 1. FIG. It is an example of the screen operated in the first embodiment. It is a figure which shows an example of the process flow of Example 1. FIG. It is a figure which shows an example of various data handled in Example 1. FIG. It is a figure explaining an example of a pathway. It is a figure explaining an example of the gene chain extracted from a pathway. This is an example of the output screen of the first embodiment. It is a figure which shows an example of the process flow of Example 2. FIG. This is an example of the output screen of the second embodiment. It is an example of the screen operated in the third embodiment. It is a figure which shows an example of the process flow of Example 3. FIG. This is an example of the output screen of the third embodiment. This is an example of the output screen of the third embodiment.

Hereinafter, a preferred embodiment of the treatment policy planning support device according to the present invention will be described with reference to the attached drawings. In the following description and the accompanying drawings, components having the same functional configuration are designated by the same reference numerals, so that duplicate description will be omitted.

The hardware configuration of the treatment policy planning support device 100 of the embodiment will be described with reference to FIG. The treatment policy planning support device 100 is a so-called computer, and is configured by connecting a calculation unit 101, a memory 102, a storage unit 104, and a network adapter 105 so as to be able to transmit and receive signals by a bus 108. Further, the treatment policy planning support device 100 is connected to the cell analyzer 110, the gene database 111, and the drug database 112 via the network adapter 105 and the network 109 so as to be able to send and receive signals, and also to send and receive signals to and from the input unit 106 and the display unit 107. Can be connected. Here, "to be able to send and receive signals" is a state in which signals can be passed to each other or from one to the other, regardless of whether they are wired or wireless, electrically or optically. Hereinafter, each part will be described.

The calculation unit 101 is a device that executes various operations and controls the operation of each unit, specifically, a CPU (Central Processing Unit) or the like. The arithmetic unit 101 loads the program stored in the storage unit 104 and the data required by the program into the memory 102 and executes the program. The memory 102 stores a program executed by the arithmetic unit 101, the progress of arithmetic processing, and the like.

The storage unit 104 is a device for storing a program executed by the arithmetic unit 101 and data necessary for executing the program, and specifically, a recording device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), or an IC. A device that reads and writes to a recording medium such as a card, SD card, or DVD. Various data such as gene mutation data 401, gene expression level data 402, pathway data 403, and drug data 404 may be stored in advance in the storage unit 104. Examples of these various data will be described later with reference to FIG. Various data including data necessary for program execution may be transmitted and received from a network 109 such as a LAN (Local Area Network).

The network adapter 105 is for connecting the treatment policy planning support device 100 to a network 109 such as a LAN, a telephone line, or the Internet.

The cell analyzer 110 is an apparatus that analyzes a sample containing a patient's cell and outputs, for example, gene mutation data 401 and gene expression level data 402 as analysis results. The gene database 111 is a database system for storing data related to genes described in known documents and the like, for example, pathway data 403. The drug database 112 is a database system for storing data related to drugs described in known documents and the like, for example, drug data 404.

The display unit 107 is a device for displaying the execution result of the program, for example, a liquid crystal display, a touch panel, or the like. The input unit 106 is an operation device in which the operator gives an operation instruction to the treatment policy planning support device 100, and is, for example, a keyboard, a mouse, a touch panel, or the like. The mouse may be another pointing device such as a trackpad or trackball. When the display unit 107 is a touch panel, the touch panel also functions as the input unit 106. The treatment policy planning support device 100 operates by the operator operating the screen displayed on the display unit 107.

The setting screen 200, which is an example of the screen operated in the first embodiment, will be described with reference to FIG. The operator sets input data, parameters, and output data using the setting screen 200. The setting screen 200 includes a patient data input unit 201, a gene mutation designation unit 202, a gene expression level designation unit 203, a attenuation rate setting unit 204, a threshold value setting unit 205, a propagation distance setting unit 206, a score selection unit 207, and a setting button 208. Have.

The patient data input unit 201 is a box for inputting data related to a patient. For example, a patient ID that identifies a patient is input, and at least one of a gene mutation and a gene expression level, which are data related to a patient's lesion, is input. It is specified. At least one of the data designated by the patient data input unit 201, that is, the gene mutation data 401 and the gene expression level data 402, is read out in the process flow described later. In the setting screen 200 exemplified in FIG. 2, both the gene mutation and the gene expression level are specified.

The gene mutation designation unit 202 is a box in which the path to the file related to the gene mutation of the patient is designated, and the gene mutation data 401 is read out based on the path designated by the gene mutation designation unit 202. The gene expression level designation unit 203 is a box in which a path to a file related to the gene expression level of the patient is designated, and the gene expression level data 402 is read out based on the path designated by the gene expression level designation section 203.

The attenuation factor setting unit 204 is a box in which the attenuation factor D is set. The attenuation rate D is a parameter indicating the rate at which the influence of the gene mutation is attenuated among the genes of the gene chain contained in the pathway, and a real number of 0 <D <1 is set. The threshold value setting unit 205 is a box in which the threshold value Th, which is a parameter related to the gene expression level, is set. When the gene expression level data 402, for example, the ratio of the gene expression level exceeds the threshold Th, the gene is regarded as an important gene. The propagation distance setting unit 206 is a box in which the propagation distance L is set. The propagation distance L is a parameter indicating an upper limit for the length of the gene chain extracted from the pathway, and a natural number is set.

The score selection unit 207 is a check box in which either the gene score or the drug score is selected as the output score. The gene score is a value indicating the degree of association between the disease and each gene. The drug score is a value indicating the degree of association between the disease and each drug. In the setting screen 200 exemplified in FIG. 2, the gene score is selected. The case where the drug score is selected will be described later in Example 2.

The setting button 208 is a button that is pressed when various settings on the setting screen 200 are completed. When the setting button 208 is pressed, the flow of processing described below is started.

An example of the processing flow of the first embodiment will be described for each processing step with reference to FIG. Hereinafter, a case where each processing step is executed by the arithmetic unit 101 will be described. Each processing step may be executed by dedicated hardware using ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or the like.

(S301)
The calculation unit 101 reads input data and parameters. The input data is read according to, for example, the path set on the setting screen 200 of FIG. That is, at least one of the gene mutation data 401 and the gene expression level data 402 is read by the calculation unit 101.

An example of gene mutation data 401, gene expression level data 402, pathway data 403, and drug data 404 will be described with reference to FIG. The gene mutation data 401 includes items such as genes, chromosomes, positions, bases of human standard genomic sequences, patient bases, and types of displacement. That is, the chromosome and position are shown as the location where the gene mutation occurred, and the base before and after the gene mutation and the type of mutation are shown. For example, the gene Gene1 is shown to have a missense mutation in base A to base G at position 4574 of chromosome chr1.

The gene expression level data 402 includes a logarithmic item of the ratio of the gene to the expression level. That is, the ratio of the expression level of the gene collected from the tumor cell of the patient to the expression level of the gene collected from the normal cell is calculated, and the logarithmic ratio of the calculated ratio is shown for each gene. For example, the gene Gene1 has a relatively large expression ratio logarithm of 10.29, and the gene Gene2 has a relatively small expression ratio ratio logarithm of 1.39. Instead of the expression level of the gene collected from the normal cell of the patient, the average value of the expression level of the gene collected from the normal cell of a plurality of other patients may be used.

The pathway data 403 has edge data and node data. The edge data includes an ID that identifies the edge, a node name connected to the start point of the edge, and a node name connected to the end point of the edge. Node data includes items for node name and node type. Node types include genes and molecules present in vivo. Based on the edge data and the node data, a pathway showing the sequence between genes in a directed graph is constructed.

Figure 5A shows an example of the pathway. The pathway 500 exemplified in FIG. 5A includes seven nodes indicated by circled numbers and eight edges indicated by arrows connecting the nodes. That is, the pathway 500 is configured by connecting the nodes indicated by the circled numbers by the edges indicated by the arrows. The circled numbers are node names for identifying genes, and the direction of the arrow indicates the direction of expression control between genes. The actual pathway is a huge network with tens of thousands of edges and nodes.

Return to the explanation in Fig. 4. Drug data 404 includes drug and gene, gene mutation, and evidence level items. That is, the gene to be treated by a drug and the gene mutation are shown together with the level of evidence indicating the reliability of the drug efficacy.

(S302)
The calculation unit 101 extracts all genes satisfying a predetermined condition based on the input data and parameters read in S301, and stores them in the list as important genes. For example, when a gene mutation is specified in the patient data input unit 201 of the setting screen 200, all genes having the mutation are extracted. When the gene expression level is specified in the patient data input unit 201, all genes whose gene expression level ratio exceeds the threshold Th are extracted. When both the gene mutation and the gene expression level are specified, all genes satisfying at least one of the conditions are extracted.

(S303)
The arithmetic unit 101 reads out the first important gene from the list in which the gene extracted in S303 is stored. If no important gene is stored in the list, that is, if nothing is extracted in S302, the processing flow ends.

(S304)
The calculation unit 101 extracts from the pathway a gene chain in which the important gene read out from the list is the most upstream and the length is equal to or less than the propagation distance. A gene chain is a series of nodes and edges in which a node that becomes a starting point and a node that becomes an end point that can be reached by tracing a directed graph from the starting point are arranged in one direction according to the order of appearance when tracing the directed graph. The length of the gene strand is represented by the number of nodes included in the gene strand, and in the gene strand having a length of 1, the start point and the end point are the same node and the edge is not included. The propagation distance is a value set in the propagation distance setting unit 206 of the setting screen 200 exemplified in FIG. 2.

An example of a gene chain extracted from a pathway will be described with reference to FIG. 5B. FIG. 5B lists all gene strands having the gene with the circled number 1 most upstream and having a length of 5 or less from the pathway 500 exemplified in FIG. 5A. That is, the number of gene chains extracted from the pathway 500 is 12, and those in which the most downstream gene k is any of the circled numbers 1 to 7 are mixed.

(S305)
The calculation unit 101 calculates the gene chain score c (k) in the most downstream gene k for each of the gene chains extracted in S304. The gene chain score c (k) represents the degree of association between the most upstream important gene and the most downstream gene k. When the length of the gene strand is 1, the gene strand score c (k) is calculated by, for example, the following equation.

Here, a (k) is the importance of mutation in the most downstream gene k, and b (k) is the ratio of the gene expression level of the most downstream gene k, which may be a logarithm. The mutation importance a (k) is calculated by, for example, the following equation.

Here, v _j is the j-th mutation of the most upstream important gene i, f (v _j ) is the score according to the type of mutation, and e (v _j ) is the score according to the relationship of the drug to the disease. be.

In the human body, 20 kinds of amino acids are produced corresponding to gene sequences in a process called translation, and the produced amino acids are converted into proteins to perform various functions. Mutations include silent mutations that do not change the amino acid corresponding to the sequence and do not affect the living body depending on the location of the mutation, and nonsense mutations that affect the translation function of a specific sequence region and stop the production of amino acids. , The impact of mutations on translation varies. Therefore, the score f ( _vj ) of each gene mutation is set so as to reflect the scale of the amino acid change caused by the mutation. The types of mutations are missense mutations, silent mutations, nonsense mutations, etc. exemplified in the gene mutation data 401 of FIG. 4, and are given to one mutation. In addition, the drug score e (v _j ) is set so as to reflect the evidence level of the drug effective for the gene mutation v _j . The level of evidence of the drug may be read from the drug data 404 exemplified in FIG.

The calculation of the mutation importance a (k) is not limited to the number 2, and the mathematical formula may be modified. If the input data of S301 does not include a gene mutation, the mutation importance a (k) is set to 0 for all genes. Similarly, when the input data does not include the gene expression level, the ratio b (k) of the gene expression level to all genes is 1 if it is not logarithmic and 0 if it is logarithmic.

The gene chain score c (k) of a gene chain having an arbitrary length is calculated by, for example, the following formula.

Here, n is an arbitrary gene contained in the gene strand, L _kn is the distance between the gene n and the most downstream gene k, and A (L _kn , D) is a function of the distance L _kn and the attenuation factor D.

When A (L _kn , D) = D ^ (L _kn ) in the number 3, since 0 <D <1, the contribution of the mutation importance a (n) to the gene chain score c (k) is a distance. The longer the L _kn , the smaller the size, and the shorter the distance L _kn , the larger the size. The calculation of the gene chain score c (k) is not limited to the number 3, and the mathematical formula may be modified.

(S306)
The calculation unit 101 calculates the gene score s (k) in the most downstream gene k by adding the gene chain score c (k) calculated in S305 for each of the most downstream genes k. For example, the gene scores for the gene with the circled number 2 are calculated by adding the gene chain scores of the second and ninth gene chains among the 12 gene chains listed in FIG. 5B. Further, by adding the gene chain scores of the 5th, 6th, and 12th gene chains, the gene score of the gene of the circle number 6 is calculated. That is, for the 12 gene strands listed in FIG. 5B, the gene scores for each gene with circled numbers 1 to 7 are calculated.

(S307)
The calculation unit 101 determines whether or not all of the important genes stored in the list in S302 have been read out. If all the important genes are read from the list, the process proceeds to S309, and if the unread important genes remain, the process is returned to S304 via S308.

(S308)
The arithmetic unit 101 reads out the next important gene from the list in which the gene extracted in S303 is stored.

(S309)
The calculation unit 101 creates a correspondence table between the gene k and the gene score s (k) and displays it on the display unit 107. In the correspondence table, it is desirable that the gene scores s (k) are arranged in descending order.

An example of the output screen 600 displayed in S309 will be described with reference to FIG. The output screen 600 includes a disease cause candidate gene list 601 in which genes are listed in the order in which they are presumed to have a high degree of association with the disease. That is, the disease cause candidate gene list 601 is a correspondence table created in S309, in which genes k are arranged in descending order of the value of the gene score s (k).

According to the processing flow described above, genes are listed in descending order of relevance to the patient's disease based on at least one of the gene mutation data and the gene expression level data obtained by analyzing the cells at the lesion site of the patient. Will be done. The operator or the like can formulate a treatment policy suitable for each patient by confirming the genes listed in descending order of the degree of association with the disease.

In Example 1, the calculation of the degree of association between the disease and the gene was described. In Example 2, the calculation of the degree of association between the disease and the drug will be described. That is, a doctor or the like who is an operator can select a drug suitable for an individual patient by confirming the degree of association between the disease and the drug. Since the hardware configuration of the second embodiment is the same as that of the first embodiment, the description thereof will be omitted.

An example of the processing flow of the second embodiment will be described with reference to FIG. 7. Since S301 to S305, S307, and S308 are the same processes as in the first embodiment, the description thereof will be omitted, and S701 added between S304 and S305 and S702 and S703 replaced with S306 and S309 will be described.

(S701)
The calculation unit 101 determines whether or not a drug whose treatment target is the most downstream gene k is in the drug data 404. If there is a drug, the treatment proceeds to S305, and if there is no drug, the treatment proceeds to S307. When the processing is advanced to S305, the gene chain score c (k) in the most downstream gene k is calculated in the same manner as in Example 1. Further, when the processing is advanced to S307, the gene chain score c (k) is not calculated and c (k) = 0.

(S702)
The calculation unit 101 calculates the drug score s (m) of the drug m by adding the gene chain score c (k) calculated in S305 for each drug m whose treatment target is the most downstream gene k. When there are a plurality of drugs for which the most downstream gene k is to be treated, the gene chain score c (k) calculated in S305 is added to the drug score s (m) of each drug.

(S703)
The calculation unit 101 creates a correspondence table between the drug m and the drug score s (m) and displays it on the display unit 107. In the correspondence table, it is desirable that the drug scores s (m) are arranged in descending order.

An example of the output screen 800 displayed in S703 will be described with reference to FIG. The output screen 800 includes a recommended drug list 801 in which the drugs are listed in descending order of relevance to the disease. That is, the recommended drug list 801 is a correspondence table created in S703, in which the drugs m are arranged in descending order of the value of the drug score s (m). The recommended drug list 801 may include a column displaying a target gene, which is a gene targeted for treatment by the drug m.

Based on at least one of the gene mutation data and gene expression level data obtained by analyzing the cells at the lesion site of the patient according to the processing flow described above, the drugs are listed in descending order of relevance to the patient's disease. Will be done. The operator or the like can select a drug suitable for each patient by checking the drugs listed in descending order of the degree of relevance to the disease.

In Example 1 and Example 2, it was described that the gene score and the drug score are calculated by using the pathway data as it is. Pathway data is a mixture of highly reliable interactions and unreliable interactions with insufficient evidence. In addition, the literature on regulation between genes is increasing day by day, and new interactions are frequently added or existing interactions are modified. Under such circumstances, the gene score and drug score calculated by the treatment policy planning support device are required to be as robust as possible against local changes in pathway data.

Therefore, in Example 3, it will be described that the calculation of the drug score is repeated while randomly invalidating a part of the pathway data to evaluate the robustness of the drug score against the local change of the pathway data. Since the hardware configuration of the third embodiment is the same as that of the first embodiment, the description thereof will be omitted.

The setting screen 900, which is an example of the screen operated in the third embodiment, will be described with reference to FIG. 9. The operator sets input data, parameters, and output data using the setting screen 900. On the setting screen 900, an invalidation ratio setting unit 901 and a repetition count setting unit 902 are added to the setting screen 200 of the first embodiment.

The invalidation ratio setting unit 901 is a box in which the invalidation ratio C is set. The invalidation ratio C is a parameter indicating the ratio of the number of invalidated edges to the total number of edges included in the pathway, and a value of 0% <C <100% is set. The repetition count setting unit 902 is a box in which the repetition count x is set. The number of repetitions x is the number of times that the drug score is calculated while randomly disabling a part of the pathway. That is, the drug score for the number of times set by the repetition number setting unit 902 is calculated.

An example of the processing flow of the third embodiment will be described with reference to FIG. Since S301 to S305, S307, S308, S701, and S702 are the same processes as in the second embodiment, the description thereof will be omitted, and they will be replaced with S1001 and S1002 added after S302 and S1003 and S703 added after S307. S1004 will be described.

(S1001)
The arithmetic unit 101 repeats the loop from S1001 to S1003 x times.

(S1002)
The calculation unit 101 invalidates the edge included in the pathway data by the ratio C. That is, when the number of edges included in the pathway data is N, CN / 100 edges are randomly selected and invalidated. Edge invalidation is a process of removing edges randomly selected from all edges included in the pathway data. For example, if the edge between the circled

numbers

4 and 6 is invalidated in the pathway 500 of FIG. 5A, the 5th and 12th gene strands among the gene strands listed in FIG. 5B will not be extracted in S304. As a result, the drug score of the drug for which the gene of the circle number 6 is treated is lowered.

(S1003)
The arithmetic unit 101 returns the process until the loop from S1001 to S1003 is repeated x times. Since the drug score s (m) for the drug m is calculated each time the loop is repeated, the drug score calculated when the number of executions _y is expressed as sy (m).

(S1004)
The calculation unit 101 creates a correspondence table between the drug m and the average drug score, and displays it on the display unit 107. The average drug score μ _y (m) at the time of the number of executions y is calculated by the following equation.

The calculated average value is used in the correspondence table between the drug m and the drug score. By confirming the drug m having a relatively high average value of the drug score _sy (m), the operator or the like can select a drug suitable for each patient.

Further, the standard deviation σ _y (m) at the time of the number of executions y of the drug score s _y (m) may be calculated by the following equation and displayed for each drug m.

By confirming the drug m having a relatively small standard deviation of the drug score _sy (m), the operator or the like can select a drug having a highly robust score. Further, robustness may be evaluated by a value obtained by dividing the average value of the drug score _sy (m) by the standard deviation.

An example of the output screen 1100 displayed in S1004 will be described with reference to FIGS. 11 and 12. The output screen 1100 includes a recommended drug list 1101 and a drug score graph 1102 in which drugs are listed in descending order of relevance to the disease. The recommended drug list 1101 is a correspondence table created in S1004, in which the drugs m are arranged in descending order of the average value of the drug scores _sy (m). The recommended drug list 1101 may include a display button for displaying a target gene for the drug m and a detailed report.

When the display button included in the recommended drug list 1101 is pressed, the drug score graph 1102 is displayed as a detailed report on the drug m. In the drug score graph 1102, the vertical axis is the drug score sy (m) and the horizontal axis is the number of executions _y , and the transition of the drug score _sy (m) calculated each time the loop from S1001 to S1003 is repeated. Is shown. Since the case where the display button for the drug m1 is pressed is exemplified in FIG. 11, the vertical axis of the drug score graph 1102 is the drug score _sy (m1). The drug score graph 1102 may display the ranking between drugs and the value of the drug score. FIG. 11 shows an example in which the rank is displayed on the upper side of the data points of the drug score graph 1102 and the value of the drug score is displayed on the lower side. Further, the average value or standard deviation calculated by the

equation

4 or 5 may be displayed in the margin of the output screen 1100.

FIG. 12 illustrates a screen in which the invalidation edge ID list 1201 is displayed when the cursor is moved to the vicinity of the data point of the drug score graph 1102. The invalidated edge ID list 1201 lists the IDs of the edges that have been invalidated when the number of executions is y. By checking the invalidation edge ID list 1201 and grasping the invalidated edge when the number of executions y, the operator can investigate the cause when the order of the drug of interest is low.

According to the processing flow described above, the drugs are listed in descending order of the degree of relevance to the patient's disease based on at least one of the gene mutation data and the gene expression level data, as in Example 2. The operator or the like can select a drug suitable for each patient by checking the drugs listed in descending order of the degree of relevance to the disease. Furthermore, the calculation of the drug score is repeated with random invalidation of a part of the pathway data, and the mean value and standard deviation of the calculation result are displayed, so that the robustness of the drug score against local changes in the pathway data can be determined. Can be evaluated. The loop from S1001 to S1003 described in Example 3 is not limited to the calculation of the drug score, and may be applied to the calculation of the gene score described in Example 1.

The plurality of embodiments of the present invention have been described above. The present invention is not limited to the above embodiment, and the components can be modified and embodied without departing from the gist of the invention. Further, a plurality of components disclosed in the above embodiment may be appropriately combined. Further, some components may be deleted from all the components shown in the above embodiment.

100: Treatment policy planning support device, 101: Calculation unit, 102: Memory, 104: Storage unit, 105: Network adapter, 106: Input unit, 107: Display unit, 108: Bus, 109: Network, 110: Cell analyzer , 111: Gene database, 112: Drug database, 200: Setting screen, 201: Patient data input unit, 202: Gene mutation specification unit, 203: Gene expression level specification unit, 204: Decrease rate setting unit, 205: Threshold setting unit , 206: Propagation distance setting unit, 207: Score selection unit, 208: Setting button, 401: Gene mutation data, 402: Gene expression level data, 403: Pathway data, 404: Drug data, 500: Pathway, 501: Gene strand , 600: Output screen, 601: Disease cause candidate gene list, 800: Output screen, 801: Recommended drug list, 900: Setting screen, 901: Invalidation ratio setting unit, 902: Repeat count setting unit, 1100: Output screen, 1101: Recommended drug list, 1102: Drug score graph, 1201: Invalidation edge ID list

Claims

It is a treatment policy planning support device that displays data that supports the planning of treatment policies.
A data acquisition unit that acquires gene mutation data or gene expression level data obtained by analyzing cells at the lesion site of a patient,
A storage unit that stores pathway data, which is pathway data that represents the sequence between genes in a directed graph.
A treatment policy planning support device comprising a score calculation unit for calculating a gene score indicating the degree of association between a disease and a gene based on the gene mutation data or the gene expression level data and the pathway data.
The treatment policy planning support device according to claim 1.
The storage unit further stores drug data including the drug and the gene to be treated by the drug.
The treatment unit is characterized by calculating a drug score indicating the degree of association between a disease and a drug based on the drug data together with the gene mutation data or the gene expression level data and the pathway data. Policy planning support device.
The treatment policy planning support device according to claim 2.
A treatment policy planning support device further comprising a display unit that lists and displays the drugs in descending order of the drug score.
The treatment policy planning support device according to claim 1.
A treatment policy planning support device further comprising a display unit that lists and displays the genes in descending order of the gene score.
The treatment policy planning support device according to claim 1.
The score calculation unit is a treatment policy planning support device characterized in that the degree of influence of a gene mutation on a living body is calculated and the degree of influence is used for calculating the gene score.
The treatment policy planning support device according to claim 1.
The score calculation unit extracts from the pathway data a gene strand in which an important gene, which is a gene satisfying the conditions determined by the gene mutation data or the gene expression level data, is the most upstream, and of the important gene and the gene strand. The feature is that the gene chain score indicating the degree of association with the most downstream gene is calculated for each gene chain, and the gene score is calculated by adding the gene chain scores of the gene chains in which the most downstream gene is the same gene. Treatment policy planning support device.
The treatment policy planning support device according to claim 1.
The score calculation unit is a treatment policy planning support device characterized in that the calculation of the gene score is repeated while randomly invalidating a part of the edge included in the pathway.
The treatment policy planning support device according to claim 7.
The score calculation unit calculates the average value of the gene scores that are repeatedly calculated.
A treatment policy planning support device further comprising a display unit that lists and displays the genes in descending order of the average value of the gene scores.
The treatment policy planning support device according to claim 7.
The score calculation unit is a treatment policy planning support device characterized by calculating a standard deviation of the gene score that is repeatedly calculated.
The treatment policy planning support device according to claim 7.
A treatment policy planning support device further comprising a display unit that displays a graph showing a transition of the gene score that is repeatedly calculated.
The treatment policy planning support device according to claim 10.
The display unit is a treatment policy planning support device characterized by displaying an invalidated edge on the graph.