Therapeutic agent selection and / or clinical trial enrollment determination assistance system

Abstract

To provide a technique for assisting selection of a therapeutic agent or determination of eligibility for clinical trial enrollment on the basis of data of a cancer gene panel test using a nucleic acid sequence analysis device.SOLUTION: Provided is a therapeutic agent selection and / or clinical trial enrollment determination assistance system for assisting selection of a therapeutic agent or determination of eligibility for clinical trial enrollment on the basis of the results of analysis performed using a nucleic acid sequence analysis device on a sample collected from a subject undergoing a cancer gene panel test, the system comprising search processing means, the search processing means refers to a database in which clinical information including information on therapeutic agents or clinical trials, and information on specific gene variants to which the therapeutic agents or the clinical trials are applicable are recorded in association with each other, and searches for and identifies, from among gene variants identified by variant calling through analysis of data output from the nucleic acid sequence analysis device, a gene variant which is the same as the specific gene variant or which is inferred to have the same influence on the function of a gene product resulting from genetic mutation as that of the specific gene variant.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a technology for processing test data from a cancer gene panel test using a base sequence analyzer. [Background technology]

[0002] In order to receive cancer genomic medicine in Japan, the results of genetic analysis from cancer gene panel testing (cancer genome profiling testing) must be reviewed by an expert panel. If the test results do not detect any genetic mutations in the patient's genome that are relevant to the clinical trial, they cannot proceed to clinical trial entry, and currently only about 10-20% of patients reach clinical trials, which is a major issue. Regarding the above issues, many of the currently popular cancer gene panel tests are "non-institutional tests," making detailed gene mutation analysis difficult. This makes it difficult to implement appropriate measures. In particular, the interpretation of the allele frequency of gene mutations (the incidence of gene mutations in a certain population) listed in the analysis report is unclear, making it impossible to consider improvement plans to improve clinical trial success rates.

[0003] Meanwhile, technology has been proposed for searching for clinical trial candidates, with the entity conducting the clinical trial as the user (Patent Document 1). The technology described in Patent Document 1 is a method for determining clinical trial candidates, and searches a database in which patient test statuses are registered to search for patients who meet the clinical trial registration conditions. The technology described in Patent Document 1 is advantageous in that it allows the entity conducting the clinical trial to efficiently find clinical trial candidates, but it cannot be used to solve the problems mentioned above.

[0004] Furthermore, a database constructed by collecting information on gene mutations and clinical trials has been proposed (Non-Patent Document 1). By inputting a gene mutation into the database of Non-Patent Document 1 and searching it, it is possible to obtain clinical trial information applicable to that gene mutation. However, no attempt has been made to incorporate such a database search system into the analysis pipeline of cancer gene panel testing. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-184371 [Non-patent literature]

[0006] [Non-Patent Document 1] Medical Pharmacy, 48(11)500-506(2022) Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention aims to provide a new technology that supports the selection of therapeutic drugs or the decision on whether or not to enter a clinical trial based on the analysis results of a base sequence analyzer of a sample collected from a subject undergoing cancer gene panel testing. [Means for solving the problem]

[0008] The present invention for solving the above problems is as follows.

[0009] [1] A therapeutic drug selection and / or clinical trial entry decision support system that supports the selection of therapeutic drugs or the decision on whether to enter a clinical trial based on the analysis results of a base sequence analyzer of samples collected from subjects undergoing cancer gene panel testing; The system comprises a search processing means; The search processing means Refer to a database in which clinical information, including information on therapeutic drugs or clinical trials, and information on specific gene mutations to which the therapeutic drugs or clinical trials are applicable are linked and recorded; Among the genetic mutations called as variants by analyzing the data output from the base sequence analyzer, is identical to the specific genetic mutation; or A therapeutic drug selection and / or clinical trial entry decision support system that searches for and identifies gene mutations that are presumed to have the same effect on the function of the gene product.

[0010] [2] The system described in [1], further comprising an annotation means for linking and recording the clinical information to the genetic mutation identified by the search processing means.

[0011] [3] The system comprises a base calling means, an alignment means, and a variant calling means; The base calling means, based on the data primarily acquired by the base sequence analyzing device, calling base sequence information according to predetermined base calling criteria; the alignment means aligns the base sequence information called by the base calling means to a reference sequence; The system described in [1] or [2], wherein the variant calling means calls genetic mutations in accordance with predetermined variant calling conditions based on the differences between the base sequence information and the reference sequence identified by alignment by the alignment means.

[0012] [4] The system includes a base calling condition change acceptance means and / or a variant calling condition change acceptance means; the base calling condition change accepting means accepts a change to the base calling conditions; The variant calling condition change acceptance means accepts changes to the variant calling conditions. [3] A therapeutic drug selection and / or clinical trial entry decision support system according to the present invention.

[0013] [5] The system includes a communication unit, the database is an external database recorded in an external device, The search processing means is configured to perform a reference to the external database by communicating with the external device through the communication unit. A therapeutic drug selection and / or clinical trial entry decision support system according to any one of [1] to [4].

[0014] [6] The system includes a communication unit, a storage unit, an external information acquisition unit, and a database update unit; The database is an internal database recorded in the storage unit; the external information acquisition means acquires information of an external database recorded in an external device through the communication unit and records the information in the storage unit; a database update means for updating the internal database by reflecting the information acquired by the external information acquisition means; The external database is a database in which clinical information including information on therapeutic drugs or clinical trials and information on specific gene mutations applicable to the therapeutic drugs or clinical trials are linked and recorded. A therapeutic drug selection and / or clinical trial entry decision support system according to any one of [1] to [5].

[0015] [7] The system includes a genetic mutation evaluation means; the genetic mutation evaluation means evaluates whether the genetic mutation called by the variant calling means is a somatic mutation or a germline mutation; A therapeutic drug selection and / or clinical trial entry decision support system according to any one of [1] to [6].

[0016] [8] The system includes a list data creation means and an extraction / sorting means; the list data creation means creates list data of variant-called genetic variations; The extraction / sorting means has the function of performing one or more of the processes specified in the following (A) to (C) on the list data, the therapeutic drug selection and / or clinical trial entry decision support system described in any one of [1] to [7]. (A) Of the variant-called genetic variations, only those genetic variations identified by the search processing means are extracted. (B) Among the variant-called genetic variations, genetic variations identified by the search processing means are sorted so as to be placed at the top of the list data. (C) Among the variant-called gene mutations, those for which one or more analysis quality parameters obtained by the base sequence analysis using the base sequence analyzer satisfy specific conditions are extracted.

[0017] [9] Provides means for setting conditions; The condition setting means receives an input operation by a user, The type of analysis quality parameter to be extracted in (C), and / or The specific conditions regarding the analysis quality parameters, which are the extraction criteria in (C). The therapeutic drug selection and / or clinical trial entry decision support system described in [8] sets the above.

[0018]

[10] A method for supporting the selection of a therapeutic drug and / or the decision to enter a clinical trial, which supports the selection of a therapeutic drug or the decision to enter a clinical trial based on the analysis results of a base sequence analyzer of a sample collected from a subject of a cancer gene panel test; The method comprises a computer-implemented search processing step; In the search processing step, Refer to a database in which clinical information, including information on therapeutic drugs or clinical trials, and information on specific gene mutations applicable to the therapeutic drugs or clinical trials are linked and recorded; Among the genetic mutations called as variants by analyzing the data output from the base sequence analyzer, is identical to the specific genetic mutation; or A method for supporting the selection of therapeutic drugs and / or clinical trial entry decisions, which searches for and identifies gene mutations that are presumed to have the same effect on the function of the gene product.

[0019]

[11] A therapeutic drug selection and / or clinical trial entry decision support program that supports the selection of therapeutic drugs or the decision on whether to enter a clinical trial based on the analysis results of a base sequence analyzer of samples collected from subjects undergoing cancer gene panel testing; The program causes a computer to function as a search processing means; The search processing means Refer to a database in which clinical information, including information on therapeutic drugs or clinical trials, and information on specific gene mutations applicable to the therapeutic drugs or clinical trials are linked and recorded; Among the genetic mutations called as variants by analyzing the data output from the base sequence analyzer, is identical to the specific genetic mutation; or A program to support therapeutic drug selection and / or clinical trial entry decisions by searching for and identifying gene mutations that are presumed to have the same effect on the function of the gene product. [Effects of the Invention]

[0020] According to the present invention, it is possible to easily and quickly match therapeutic drugs or clinical trials to be applied to patients who have undergone cancer gene panel testing. When the present invention is applied to support the decision-making process for clinical trial entry, it is possible to improve the clinical trial entry rate. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a block diagram schematically illustrating an outline of a system configuration according to a first embodiment. [Figure 2] 1 is a schematic diagram illustrating an example of the hardware configuration of an information processing device and a terminal according to a first embodiment. [Figure 3] FIG. 10 is a schematic diagram illustrating an example of the hardware configuration of an information processing device and a terminal according to a second embodiment. [Figure 4] FIG. 10 is a schematic diagram illustrating an example of the hardware configuration of an information processing device and a terminal according to a third embodiment. [Figure 5]FIG. 10 is a schematic diagram illustrating an example of the hardware configuration of an information processing device and a terminal according to a fourth embodiment. [Figure 6] FIG. 1 is a schematic diagram showing a social implementation model of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] The present invention will be described in detail below with reference to the drawings. In the embodiments of the present invention, A (numerical value) to B (numerical value) means A or more and B or less. Furthermore, the preferred and more preferred embodiments exemplified below can be used in appropriate combinations with each other regardless of expressions such as "for example," "preferred," and "more preferred." Furthermore, the descriptions of numerical ranges are merely examples, and ranges obtained by appropriately combining the upper and lower limits of each range and the numerical values ​​of the examples can also be preferably used (for example, when A to B or C to D is described, the combinations A to D or C to B can be used). Furthermore, terms such as "contain" or "comprise" may be interpreted as "essentially consisting of" or "consisting only of."

[0023] In this embodiment, the configuration and operation of a therapeutic drug selection and / or clinical trial entry decision support system will be described, but similar effects can also be achieved with the methods (steps), devices, computer programs, etc. that are executed. Furthermore, the program in this embodiment may be provided as a non-transitory recording medium that can be read by a computer (computer device), or may be provided so as to be downloadable from an external server.

[0024] Furthermore, in this embodiment, the term "unit" may include a combination of hardware resources implemented by circuits in a broad sense, and software information processing that can be specifically realized by these hardware resources.

[0025] In this embodiment, information is represented by, for example, the physical value of a signal value representing voltage or current, the high or low value of a signal value as a collection of binary bits consisting of 0 or 1, or quantum superposition, so-called quantum bits, and communication and calculations can be performed on a circuit in a broad sense.

[0026] The broad definition of a circuit refers to a circuit realized by appropriately combining a circuit, circuitry, processor, memory, etc. In other words, it includes a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), etc. [Example]

[0027] Hereinafter, a first embodiment of the present invention will be described with reference to FIGS.

[0028] <Cancer gene panel testing> Cancer gene panel testing is a test that simultaneously analyzes dozens to hundreds of cancer-related genes to identify abnormalities present in a patient's tumor. Understanding the genetic profile of a specific cancer patient can provide information for selecting therapeutic drugs and clinical trials that are appropriate for the patient's genetic mutations.

[0029] Cancer gene panel testing is based on the analysis results obtained by the base sequence analysis device 2. The system of this embodiment supports the selection of a therapeutic drug or the determination of whether or not to enter a clinical trial based on the analysis results obtained by the base sequence analysis device 2 of a sample collected from a subject for cancer gene panel testing.

[0030] The base sequence analyzer 2 is a device used to read the base sequence of genes contained in a sample. The base sequence analyzer 2 according to this embodiment is preferably a next-generation sequencer that performs sequencing using next-generation sequencing technology, or a third-generation sequencer. Next-generation sequencers are a group of base sequence analyzers that have been developed in recent years, and have dramatically improved analytical capabilities by performing massive parallel processing of clonally amplified DNA templates or single DNA molecules in a flow cell.

[0031] Furthermore, the sequencing technology that can be used in this embodiment may be a sequencing technology that obtains multiple reads by reading the same region in duplicate (deep sequencing).

[0032] Examples of sequencing technologies that can be used in this embodiment include ion semiconductor sequencing, pyrosequencing, sequencing-by-synthesis using reversible dye terminators, sequencing-by-ligation, and sequencing by oligonucleotide probe ligation, which are based on sequencing principles other than the Sanger method and can obtain a large number of reads per run.

[0033] The sequencing primers used for sequencing are not particularly limited and are appropriately set based on a sequence suitable for amplifying the target region. Furthermore, the reagents used for sequencing may be selected appropriately depending on the sequencing technology and the base sequence analyzer 2 used. There are no limitations on the procedures from pretreatment to sequencing, and conventional methods can be used.

[0034] <System configuration> 1 shows a schematic diagram of a therapeutic drug selection and / or clinical trial entry decision support system 0 according to this embodiment. In this embodiment, the support system 0 comprises an information processing device 1 and a base sequence analysis device 2. Because quality information on the analysis run by the base sequence analysis device can be included in the consideration materials for the expert panel, it is preferable that the support system 0 includes the base sequence analysis device 2, as in this embodiment.

[0035] In a more preferred embodiment, the information processing device 1 and the base sequence analysis device 2 are installed in the same facility. This allows the facility staff to understand the entire analysis procedure for cancer gene panel testing and to set the analysis conditions appropriately.

[0036] Although not shown, it is also possible to adopt an embodiment in which the support system 0 does not include the base sequence analysis device 2 as a system component. In other words, it is also possible to adopt an embodiment in which raw data of base sequence analysis performed by an external institution is received and analyzed in detail by the support system 0.

[0037] The information processing device 1 can be a general-purpose server computer or a personal computer (computer device), and the support system 0 can also be configured by implementing the functional components described below in multiple computers.

[0038] As shown in Fig. 1, the information processing device 1 of this embodiment is configured to be able to communicate with an external device 3 via a communication network NW. As will be described later, the information processing device 1 is configured to communicate with the external device 3. In this specification, the term "external device" broadly means a device located outside the support system 0. The external device 3 may be under the management of the same organization as the information processing device 1 and may communicate with the information processing device 1 via intranet communication. Alternatively, the external device 3 may be under the management of an organization different from the information processing device 1 and may communicate with the information processing device 1 via internet communication.

[0039] In this embodiment, the communication network NW is an IP (Internet Protocol) network, but there is no restriction on the type of communication protocol, and there is also no restriction on the type or scale of the network, and as mentioned above, it may be the Internet or an intranet.

[0040] <Hardware configuration> 2 is a diagram showing an example of the hardware configuration of the information processing device 1. As shown in FIG. 2, the information processing device 1 includes a control unit 101, a storage unit 102, a communication unit 103, an input unit 104, and an output unit 105.

[0041] The control unit 101 includes one or more processors such as a CPU or an MPU, and controls the overall operation and processing of the information processing device 1 by executing the support system according to the present invention, the OS, and other applications.

[0042] The memory unit 102 is a storage device such as an HDD or optical disk, or a semiconductor memory element such as an SSD, ROM, or RAM, and stores various data such as the training support program of the present invention and data and software used when the control unit 101 executes processing based on the program.

[0043] The communication unit 103 is an interface for inputting and outputting data to and from external devices. The communication unit 103 functions to connect the information processing device 1 to a communication network NW. Specifically, a LAN (Local Area Network) or a WAN (Wide Area Network) can be adopted. The communication unit 103 functions as an interface for acquiring raw data of base sequence information from the base sequence analysis device 2. The connection between the information processing device 1 and the base sequence analysis device 2 may be wired or wireless.

[0044] The input unit 104 is an input device such as a keyboard, a mouse, a touch panel, an OCR (Optical Character Reader), or a microphone, and inputs operation requests from the user to the control unit 101.

[0045] The output unit 105 is an output device such as a display, a projector, or a speaker, and displays the results of processing by the control unit 101, etc.

[0046] <Functional configuration> 2, the information processing device 1 includes, as its functional configuration, a base calling means 110, a quality check means 111, a quality standard change receiving means 112, an alignment means 113, a variant calling means 114, a variant calling condition change receiving means 115, a search processing means 116, an annotation means 117, a gene mutation evaluation means 118, a list data creation means 119, an extraction / sorting means 120, and a report creation means 121. These are information processing performed by software stored in the storage unit 102, specifically realized by hardware such as the control unit 101 and the storage unit 102.

[0047] In the base sequence analyzer 2, a different fluorescent substance corresponds to each type of base, and the bases are read based on the wavelength and intensity of the fluorescence. In the base sequence analyzer 2, numerical values ​​indicating the fluorescence intensity are recorded as raw data. The base call means 110 performs base calling, converting the fluorescence intensity data into DNA sequence data of adenine, thymine, guanine, and cytosine. Generally, the base-called data is saved in a text format called FASTQ.

[0048] The quality check means 111 is a means for checking the quality of the analysis (NGS-Run in the case of a next-generation sequencer) by the base sequence analyzer 2 in accordance with predetermined quality standards. Examples of quality standards adopted by the quality check means 111 are shown below.

[0049] The quality criterion may be the percentage of clusters that pass a passing filter, and if this percentage is equal to or less than a predetermined threshold, the sequencing quality may be determined to be low. The threshold value can be set to any value preferably between 40% and 80%, more preferably between 50% and 70%, even more preferably between 55% and 65%, and even more preferably 60%.

[0050] The quality criterion may be the average error rate of reads 1 to 4. In some embodiments, the quality of the sequencing may be determined to be low if the average error rate is equal to or less than a predetermined threshold. The threshold value can be set to any value preferably between 0.5% and 10%, more preferably between 1% and 5%, even more preferably between 1% and 3%, and even more preferably 2%.

[0051] The quality criterion may be the percentage of bases with a quality score exceeding Q30. In an embodiment, the quality of sequencing may be determined to be low if the percentage of bases with a quality score exceeding Q30 is equal to or less than a predetermined threshold. The threshold value can be set to any value preferably between 65% and 95%, more preferably between 75% and 90%, even more preferably between 80% and 90%, and even more preferably 85%.

[0052] As quality criteria, it is preferable to adopt one or more of the following: the proportion of clusters that pass the pass filter, the average error rate from read 1 to read 4, and the proportion of bases with a quality score exceeding Q30; and it is particularly preferable to adopt all three.

[0053] In this embodiment, the control unit 101 includes a quality standard change receiving means 112. By including the quality standard change receiving means 112, the operator can arbitrarily change the quality standard, enabling quality control of the cancer gene panel test.

[0054] In a preferred embodiment, if the quality is determined to be low by the check by the quality check means 111, the data is deemed inadequate as a basis for reporting the results of the cancer gene panel test, and the analysis is discontinued. By adopting such an embodiment, it is possible to ensure that the results of the cancer gene panel test are reported with high accuracy based on high-quality data.

[0055] The alignment means 113 is a means for aligning a read sequence to a reference sequence. Alignment refers to a process of aligning each read sequence to a region that has a high degree of match with the nucleic acid sequence of the reference sequence used. The reference sequence is a sequence to which the read sequence is mapped in order to determine which region of the gene the read sequence corresponds to and which mutation of the gene the read sequence corresponds to.

[0056] The information on the reference sequence referred to by the alignment means 113 may be stored in the storage unit 102 or in an external database. When referring to a reference sequence stored in an external database, the information processing device 1 accesses the external database via the communication unit 103 and the communication network NW.

[0057] The variant calling means 114 is a means for calling a genetic mutation based on the difference between the base sequence information and the reference sequence identified by the alignment by the alignment means 113, in accordance with predetermined variant calling conditions.

[0058] Variant calling conditions are used to exclude unreliable differences from the reference sequence found by alignment. Variants resulting from artifacts during analytical testing or deamination of sample DNA are excluded as unreliable and only likely variants are called.

[0059] When the sample subjected to analysis is a blood specimen, the deamination criteria may be adopted as the variant calling conditions. The criteria for determining whether an artifact is due to deamination may be adopted, whereby if all of the following three conditions are met, it is determined that the artifact is due to deamination. (Condition 1) The detected mutation is a mutation from cytosine (C) to thymine (T) and / or guanine (G) to adenine (A) relative to the reference sequence. (Condition 2) The detection frequency of the detected mutation is equal to or less than a predetermined threshold. (Condition 3) The allele count of the detected mutation is equal to or less than a predetermined threshold.

[0060] The threshold value of condition 2 can be set to any value preferably between 0.05% and 1%, more preferably between 0.1% and 0.5%, even more preferably between 0.2% and 0.4%, and even more preferably 0.3%.

[0061] The threshold value of the condition 3 can be set to any number preferably less than 10, more preferably any number equal to or less than 7, even more preferably any number equal to or less than 5, and even more preferably 5.

[0062] Alternatively, variant calling criteria may be adopted that consider mutations commonly detected in multiple samples analyzed under the same experimental environment to be artifacts, since the experimental environment is more likely to be prone to artifacts than the possibility that patient or tissue samples with the same mutation were analyzed by chance.

[0063] Specifically, a mutation that is commonly detected in preferably two or more samples, more preferably five or more samples, and even more preferably ten or more samples analyzed under the same experimental environment may be determined to be an artifact.

[0064] In this embodiment, the control unit 101 includes a variant calling condition change receiving means 115. By including the variant calling condition change receiving means 115, the operator can arbitrarily change the conditions for extracting likely variants, enabling quality control of cancer gene panel testing.

[0065] The search processing means 116 is a means for referring to a database in which genes for which therapeutic drugs or clinical trials are applicable are recorded, and searching for and identifying genetic mutations that are applicable to these genes. In this embodiment, the search processing means 116 refers to a database 31 in which clinical information including information on therapeutic drugs or clinical trials is linked to information on specific genetic mutations for which the therapeutic drugs or clinical trials are applicable. In this specification, the term "specific gene mutation" refers to a gene mutation that is recorded in a database as a gene mutation for which a therapeutic drug or clinical trial is applicable.

[0066] The database 31 may be a database constructed by collecting genomic information of cancer patients and clinical information including the effects of therapeutic drugs and prognosis from hospitals and research facilities at each institution.

[0067] Furthermore, the database 31 may be a database constructed by collecting details of genetic mutations and related clinical trial information, such as the source of the clinical trial, the name of the clinical trial, the last update date, the clinical trial number, the clinical trial facility, the drugs used in the clinical trial and their pharmacological effects, the type of cancer targeted, the target age group, the line of treatment targeted, eligibility conditions, exclusion conditions, recruitment status, and contact information.

[0068] Examples of the database 31 include a cancer knowledge database (CKDB) and Hirodai-DB (Non-Patent Document 1).

[0069] 1 shows only one database 31. However, the search processing means 116 can be configured to be able to comprehensively refer to a plurality of databases.

[0070] Next, the search processing means 116 searches for and identifies mutations (a) or (b) among the genetic mutations that have been variant-called by analyzing the data output from the base sequence analysis device 2. (a) a mutation that is identical to a specific gene mutation (b) A genetic mutation that is predicted to have the same effect on the function of the gene product as a specific genetic mutation. The "gene product" referred to here is RNA and / or protein.

[0071] Whether a genetic variant called by the variant calling means 114 falls under (b) above can be determined by utilizing a machine learning model. Such a machine learning model is trained using a machine learning algorithm. By using existing data and research results on genetic variants and their effects, the model learns patterns and correlations and acquires the ability to predict the effects of new genetic variants. Machine learning models that predict the functional effects of genetic variants are widely used in the fields of genetics and medicine, so existing models may be implemented.

[0072] Existing tools for predicting the effect of gene mutations on the function of gene products include PolyPhen-2 (Polymorphism Phenotyping v2), SIFT (Sorting Intolerant From Tolerant), CADD (Combined Annotation Dependent Depletion), GERP (Genomic Evolutionary Rate Profiling), DeepVariant, etc. These tools may be used to determine whether or not a mutation falls under (b).

[0073] There are no particular limitations on the specific mode of search by the search processing means 116. Typically, the search processing means 116 searches the database 31 using the genetic mutation called by the variant calling means 114 as a search query.

[0074] In a preferred embodiment, the information processing device 1 implements a machine learning model that optimizes the genetic variations called by the variant calling means 114 as search queries for searching the database 31.

[0075] If the search processing means 116 identifies a genetic mutation as falling under (a) or (b), annotation is performed by the annotation means 117. The annotation means 117 links the genetic mutation identified by the search processing means 116 with clinical information including information on therapeutic drugs or clinical trials, and records the linked information in the memory unit 102.

[0076] Among the clinical information linked to gene mutations, information on therapeutic drugs may include the name of the drug, as well as its response rate, the type of cancer it is suitable for, dosage and administration, whether it has been approved by the Ministry of Health, Labor and Welfare, side effects, and one or more information selected by the manufacturer.

[0077] The term "therapeutic drug" as used herein includes not only pharmaceuticals that have been proven to be effective in treating a disease, but also "candidate drugs for treatment" that are expected to be effective in treating a disease.

[0078] Of the clinical information linked to gene mutations, clinical trial information may include one or more of the following: source of the clinical trial, clinical trial name, last update date, clinical trial number, clinical trial facility, drugs used in the clinical trial and their pharmacological effects, target cancer type, target age, target treatment line, eligibility conditions, exclusion conditions, recruitment status, and contact information.

[0079] The information processing device 1 of this embodiment includes a genetic mutation evaluation means 118. The genetic mutation evaluation means 118 evaluates whether the genetic mutation called by the variant calling means 114 is a somatic mutation or a germline mutation.

[0080] Mutations occurring in somatic cells are called somatic mutations, and mutations occurring in germ cells are called germline mutations. Unlike somatic mutations, germline mutations can be inherited by individuals of the next generation. Therefore, if a patient to whom the method of this embodiment is applied has inherited germline mutations from their parental generations, even a sample prepared from somatic cells will contain germline mutations.

[0081] In an embodiment including the gene mutation evaluation means 118, tumor samples and non-tumor samples collected from a subject undergoing cancer gene panel testing are analyzed, and base sequence analysis is performed on each sample. The gene mutation evaluation means 118 evaluates gene mutations that are commonly called by the variant calling means 114 in both the tumor sample and the non-tumor sample as germline mutations, and evaluates gene mutations that are called only by analysis of the tumor sample as somatic mutations.

[0082] The information processing device 1 of this embodiment includes a list data creation means 119. The list data creation means 119 is a means for creating list data of variant-called gene mutations.

[0083] The information processing device 1 of this embodiment includes an extraction / sorting means 120. The extraction / sorting means 120 has a function of executing one or more of the processes specified in the following (A) to (C) on list data. (A) Of the variant-called genetic variations, only those genetic variations identified by the search processing means 116 are extracted. (B) Among the variant-called genetic variations, genetic variations identified by the search processing means 116 are sorted so as to be placed at the top of the list data. (C) Among the variant-called gene mutations, those for which one or more analysis quality parameters obtained by the base sequence analysis using the base sequence analyzer satisfy specific conditions are extracted.

[0084] The analytical quality parameters employed in (C) above are not particularly limited, but may be one or more selected from parameters 1 to 13 listed below along with an explanation of their technical significance. Note that the names of the parameters listed below are shown in parentheses after the names of the parameters output by the Illumina next-generation sequencer.

[0085] 1. Count As used herein, "count" refers to the number of observations of an alternative allele (variant allele) at a particular genomic locus in next-generation sequencing data analysis, i.e., the number of sequence reads supporting the alternative allele. This value can be determined, for example, by an allele calling algorithm called "Flow Evaluator" or a similar evaluation method. In filtering variant calling results, variants with "counts" below a predetermined threshold are deemed to have insufficient statistical evidence and are therefore deemed unreliable, and may be excluded from analysis.

[0086] 2. Coverage As used herein, "coverage" refers to the total number of times a particular genomic locus is covered by sequence reads, i.e., read depth, in next-generation sequencing data analysis. This value can be calculated, for example, using the "Flow Evaluator" algorithm or similar means. In filtering variant calling results, variants called from loci with "coverage" below a predetermined threshold may be deemed unreliable due to insufficient information to support the call.

[0087] 3. Allele frequency As used herein, "Frequency" refers to the frequency of alternative alleles (allele frequency) at a specific genomic locus in next-generation sequencing data analysis. This value is typically calculated by dividing the number of observations of alternative alleles at that locus (the "Count" above) by the total read depth (the "Coverage" above), for example, based on the observation counts obtained by the "Flow Evaluator" algorithm. In filtering variant call results, variants whose "Frequency" is below a predetermined threshold (e.g., a value set as "Minimum frequency (%)") may be deemed unreliable and excluded, considering the possibility that they represent sequencing errors or rare events with little biological significance.

[0088] 4. Probability As used herein, "probability" refers to a quality score (hereinafter sometimes referred to as "Q") that expresses the probability that a called variant is incorrect (error probability, hereinafter sometimes referred to as "e") in next-generation sequencing data analysis on a Phred scale. This score Q is generally calculated from the error probability e using the formula Q = -10log10(e). By this definition, a higher score Q indicates an exponentially smaller error probability e, indicating a higher reliability of the variant call. Conversely, a lower score Q indicates a relatively high error probability e, which tends to increase the likelihood of a false positive. For example, Q = 20 means that the error probability e is 1 / 100 (i.e., 0.01), which corresponds to 99% accuracy. Q = 30 means that the error probability e is 1 / 1000 (i.e., 0.001), which is recognized as a level of extremely high reliability, equivalent to 99.9% accuracy. In filtering variant calling results, variants with a probability below a certain threshold are excluded because they have a high error probability, i.e., low statistical reliability. Therefore, setting an appropriate threshold based on this score is important to effectively eliminate false-positive calls and ensure the reliability of analysis results.

[0089] 5. Forward reads As used herein, data items related to "forward read count" include FSAF (Flow Evaluator Alternate allele observations on the forward strand) and FSRF (Flow Evaluator Reference observations on the forward strand). FSAF refers to the number of times an alternate allele is observed among reads mapped to the forward DNA strand at a particular genomic locus. FSRF refers to the number of times a reference allele is observed among reads mapped to the forward DNA strand. These values ​​are determined, for example, by the "Flow Evaluator" algorithm and are particularly used to evaluate strand bias.

[0090] 6. Reverse reads As used herein, data items related to "reverse read count" include FSAR (Flow Evaluator Alternate allele observations on the reverse strand) and FSRR (Flow Evaluator Reference observations on the reverse strand). FSAR refers to the number of times an alternate allele is observed among reads mapped to the reverse DNA strand at a particular genomic locus. FSRR refers to the number of times a reference allele is observed among reads mapped to the reverse DNA strand. These values ​​are also determined, for example, by the "Flow Evaluator" algorithm and are used to evaluate strand bias.

[0091] 7. Strand Bias (Forward / Reverse) As used herein, "strand bias" refers to an indicator of strand bias in next-generation sequencing data analysis, which indicates whether variants detected at a particular genomic locus are favored more in either the forward or reverse DNA strand. Number of forward leads / (Number of forward leads + Number of reverse leads) Number of reverse reads / (Number of forward reads + Number of reverse reads) In an ideal situation, the number of forward and reverse reads would be nearly equal, and this value would be close to 0.5. Values ​​significantly different from 0.5 (e.g., extremely small values, such as 0.1) suggest significant bias toward either forward or reverse reads in that region (strand bias). Strand bias can occur due to specific steps in library preparation (e.g., DNA fragmentation, adapter ligation, PCR amplification), sequencing reactions, or mapping errors. Variants exhibiting strong strand bias are more likely to be technical artifacts, providing important information for assessing the reliability of called variants.

[0092] 8. Average lead quality As used herein, "average read quality" refers to the average base call quality score (e.g., Phred quality score) of a group of reads covering a specific genomic locus or its surrounding region in next-generation sequencing data analysis. This value indicates the overall reliability of the sequence data for that region. In filtering variant call results, variants called from regions with this "average read quality" below a predetermined threshold may be deemed unreliable and excluded, taking into account the possibility that they are false positives due to sequencing errors.

[0093] 9. Minimum coverage threshold As used herein, the term "minimum coverage threshold" refers to a threshold used to filter variant call results, specifying the minimum read depth (coverage) required for a particular genomic locus to allow reliable variant calling. Variants called from loci with coverage below this threshold may be subject to exclusion.

[0094] 10. Minimum allele count threshold As used herein, the term "minimum allele count threshold" refers to a threshold used to filter variant calling results, specifying the minimum number of reads (counts) required to support an alternative allele at a particular genomic locus. Variants with a number of reads supporting the alternative allele below this threshold may be subject to exclusion.

[0095] 11. Minimum allele frequency threshold (%) As used herein, the term "minimum allele frequency threshold (%)" refers to a threshold used to filter variant call results, specifying the minimum percentage (allele frequency) that alternative alleles must occupy in total reads at a particular genomic locus. Variants with an allele frequency below this threshold may be subject to exclusion.

[0096] 12. # unique start positions As used herein, "number of unique start positions" refers to the total number of unique start positions that do not overlap with the start positions on the reference genome among the reads that contributed to a variant call at a specific genomic locus in next-generation sequencing data analysis. This value serves as an indicator for evaluating the impact of duplicate reads that may occur during PCR amplification. When filtering variant call results, variants with extremely low "number of unique start positions" may be due to excessive amplification of a small number of original DNA fragments and can be excluded as being unreliable.

[0097] 13. Unique End Positions As used herein, "number of unique end positions" refers to the total number of unique end positions that do not overlap with end positions on the reference genome among the reads that contributed to variant calls at a specific genomic locus in next-generation sequencing data analysis. This value is also an indicator for evaluating the influence of PCR duplicates, and is used together with the "number of unique start positions" to evaluate the diversity and reliability of variant-supporting reads. In filtering, variants with extremely low values ​​may also be subject to exclusion.

[0098] When the extraction / sorting means 120 has the function specified in (C) above, the information processing device 1 preferably further includes a condition setting receiving means 124 so that the "analysis quality parameters" and / or "specific conditions" in (C) above can be arbitrarily set. The condition setting receiving means 124 receives an input operation by the user and sets one or both of the following: The types of analysis quality parameters to be extracted in (C) The specific conditions regarding the analysis quality parameters, which are the extraction criteria in (C)

[0099] The types of analysis quality parameters include the above-mentioned parameters 1 to 13. The condition setting receiving means 124 presents a plurality of parameters such as the above-mentioned parameters 1 to 13 to the user, receives a selection made by the user's input operation, and can set this as an extraction item.

[0100] The specific conditions regarding the analysis quality parameters can be set appropriately according to the type of analysis quality parameter and according to the input operation of the user.

[0101] It is particularly preferable that the extraction / sorting means 120 has a function of executing either the process specified in (A) or (B) above, and the process specified in (C).

[0102] In a more preferred embodiment, the extraction / sorting means 120 sorts the genetic mutations identified by the search processing means 116 so that genetic mutations that should be reported with priority are placed higher in the list data based on the clinical information linked by the annotation means 117. Criteria for determining the priority of genetic mutations that should be reported can be set as appropriate.

[0103] For example, if the clinical information linked by the annotation means 117 includes information on a therapeutic drug, the priority can be determined by weighting and scoring the efficacy rate of the therapeutic drug, the type of cancer to which it is applicable, dosage and administration, whether it has been approved by the regulatory authorities, side effects, and one or more types of information selected by the manufacturer.

[0104] In this case, the priority of genetic mutations to be reported can be determined by a machine learning model. The machine learning model refers to the patient's medical records, diagnostic information, and symptoms, as well as information on therapeutic drugs linked to genetic mutations by the annotation means 117, and sorts genetic mutations linked to information on therapeutic drugs that are presumed to be more suitable for the patient so that they are placed higher in the list data.

[0105] In addition, if the clinical information linked by the annotation means 117 includes information about clinical trials, priority can be determined by weighting and scoring one or more pieces of information selected from the clinical trial facility, the drugs used in the clinical trial and their pharmacological effects, the target cancer type, the target age, the target treatment line, eligibility conditions, exclusion conditions, and recruitment status.

[0106] In this case, too, the priority of genetic mutations to be reported can be determined by a machine learning model. The machine learning model refers to the patient's medical records, diagnostic information, and symptoms, as well as information on clinical trials linked to genetic mutations by the annotation means 117, and sorts genetic mutations linked to information on clinical trials that are presumed to be more suitable for the patient so that they are placed higher in the list data.

[0107] The report creation means 121 is a means for creating a report for reporting the results of the cancer gene panel test. The report contains a list of gene mutations that reflects the list data created as described above.

[0108] For each genetic mutation listed in the list of genetic mutations to which clinical information is linked by the annotation means 117, the report creation means 121 creates a list in which the clinical information is added to the report column for that genetic mutation.

[0109] In addition, an embodiment may be adopted in which quality information of the analysis by the base sequence analysis device 2 is included in the report created by the report creation means 121. As the quality information, one or more selected from the percentage of clusters that passed the pass filter described above, the average error rate from read 1 to read 4, and the percentage of bases with a quality score exceeding Q30 can be used.

[0110] Furthermore, items related to the variant calling conditions may be included in the report created by the report creation means 121. For example, an embodiment may include items selected from information on whether or not the mutation corresponds to condition 1 in the deamination criteria, the detection frequency of the mutation specified in condition 2, the allele count of the detected mutation specified in condition 3, and information on whether or not the same genetic mutation was detected in multiple samples analyzed under the same environment, and, if detected, information on the number of samples.

[0111] The report created by the report creation means 121 can be used as reference material for deliberation by the expert panel. The report output by the support system 0 of the present invention can effectively support the expert panel in selecting a therapeutic drug and making a decision on clinical trial entry. [Example]

[0112] Hereinafter, a second embodiment of the present invention will be described with reference to Fig. 3. Note that a description of the configuration common to the first embodiment will be omitted.

[0113] The outline of the support system 0 of embodiment 2 is the same as that shown in Figure 1, but the hardware configuration and functional configuration differ in that it is equipped with an external information acquisition means 122 and a database update means 123, and a database 311 is stored in the memory unit 102 (Figure 3).

[0114] In this embodiment, the database 311 is an internal database recorded in the storage unit 102. The external information acquisition means 122 acquires information of the external database 31 recorded in the external device 3 via the communication unit 103 and records the information in the storage unit 102. The database update means 123 reflects the information acquired by the external information acquisition means 122 in the internal database 311 to update it.

[0115] As explained in the first embodiment, the external database 31 stores clinical information, including information on therapeutic drugs or clinical trials, and information on specific gene mutations applicable to the therapeutic drugs or clinical trials, in association with each other. The support system 0 of the second embodiment is configured to transfer the clinical information and information on specific gene mutations stored in the external database 31 to the internal database 311 using the external information acquisition means 122 and the database update means 123.

[0116] In this embodiment, the database referred to by the search processing means 116 is the internal database 311 that can be updated in the above-described flow.

[0117] According to this embodiment, by integrating clinical information and information on specific gene mutations that have been stored in multiple external databases 31, a more comprehensive internal database 311 can be constructed, thereby enabling easier and faster support for selecting therapeutic drugs and deciding whether to enter clinical trials. [Example]

[0118] Hereinafter, a third embodiment of the present invention will be described with reference to Fig. 4. Note that a description of the configuration common to the first and second embodiments will be omitted.

[0119] The information processing device 1 in embodiment 3 is a web server, and information processing in the support system of the present invention is provided as a web application. In embodiment 3, raw data including information on gene mutations output from the base sequence analysis device 2 is uploaded from the user terminal device 4 to the information processing device 1 via the communication network NW. The uploaded raw data is received by the information processing device 1 and subjected to a search processing step by the search processing means 116.

[0120] When the search processing means 116 identifies a genetic mutation as falling under the above-mentioned (a) or (b), annotation is performed by the annotation means 117. The annotation means 117 links the genetic mutation identified by the search processing means 116 with clinical information including information on therapeutic drugs or clinical trials, and records the linked information in the storage unit 102.

[0121] The list data creation means 119 creates list data of variant-called genetic mutations, and the extraction / sorting means 120 generates list data that has been subjected to one or more of the processes specified in (A) to (C) above.

[0122] The information processing device 1 includes a condition setting receiving means 124 so that the "specific conditions" of the analysis quality parameters in (C) can be set arbitrarily. The condition setting receiving means 124 is configured to receive an input operation from the user terminal device 4 via the communication network NW, and to set the specific conditions to be extracted in (C).

[0123] The information processing device 1 includes an output unit 125. The output unit 125 outputs the list data that has been subjected to extraction or sorting processing as table data. The file format of the table data is not particularly limited, and examples include Excel (.xlsx, .xlsm, .xlsb), CSV (.csv), TSV (.tsv), XML, Open Document Format (.ods), etc.

[0124] The system of the third embodiment is configured so that the output table data can be downloaded via the communication network NW in response to a request from the user terminal device 4. [Example]

[0125] Hereinafter, a fourth embodiment of the present invention will be described with reference to Fig. 5. Note that a description of the configuration common to the first to third embodiments will be omitted.

[0126] While the third embodiment is provided as a web application, the fourth embodiment is provided as a native application. In the fourth embodiment, raw data including information on gene mutations output from the base sequence analysis device 2 is input to the information processing device 1. The input raw data is subjected to a search processing step by the search processing means 116. The subsequent processing and the functional configuration for realizing the processing are the same as those in the third embodiment.

[0127] <Social implementation model> A social implementation model of the present invention will be described with reference to Figure 6. The first cancer gene panel test using the support system of the present invention is performed before primary treatment or after secondary treatment. Specifically, a pathological specimen of the main lesion is obtained from a patient before primary treatment or after secondary treatment, and a cancer gene panel test using the support system of the present invention is performed. This test is performed at the patient's own expense.

[0128] If this first test reveals an actionable variant, i.e., a genetic mutation for which an applicable treatment or clinical trial is available, the test results will be provided to the attending physician as reference information, along with a recommendation that a pathological sample from the primary lesion be used when conducting a cancer genome profiling test (CGP test) covered by insurance.

[0129] On the other hand, if no actionable variants are found in the first test, it is recommended that insurance-covered CGP testing be performed using a pathology specimen different from the primary lesion.

[0130] If no additional testing is performed, the doctor will be provided with the results of the first test and a recommendation to perform an insurance-covered CGP test using a pathology specimen different from the main lesion.

[0131] When additional testing is performed, a pathological specimen different from the main lesion is obtained from the patient, and a cancer gene panel test (second time) is performed using the support system of the present invention. The results of the second test are then provided to the attending physician as reference information.

[0132] After the reference information is provided to the attending physician, an expert panel (EP) will review the case. The EP will use the test results obtained using this invention as reference information to determine the specimen type for CGP testing under health insurance coverage or to determine whether the patient should be enrolled in a clinical trial.

[0133] CGP testing under health insurance coverage has issues such as limited timing and the ability to be performed only once in a lifetime. However, by implementing this invention using the model shown in Figure 4, it becomes possible to select appropriate specimen types, enabling the effective use of valuable health insurance coverage for CGP testing. Furthermore, application of the system of this invention makes it possible to provide useful information, thereby improving the clinical trial enrollment rate.

[0134] <Methods and Programs> The present invention also relates to a method for supporting the selection of a therapeutic drug and / or the decision to enter a clinical trial. The method of the present invention is executed by the above-mentioned system for supporting the selection of a therapeutic drug and / or the decision to enter a clinical trial. The above-mentioned explanation regarding the system of the present invention can be applied to specific embodiments of the method of the present invention.

[0135] The present invention also relates to a program for supporting the selection of therapeutic drugs and / or clinical trial entry decisions. The program of the present invention causes a computer to function as each of the means of the system for supporting the selection of therapeutic drugs and / or clinical trial entry decisions described above. The above-mentioned explanation of the system of the present invention can be applied as is to specific embodiments of the program of the present invention.

[0136] If a cancer gene panel test using the support system of the present invention does not discover any gene mutations for which suitable therapeutic drugs or clinical trials are available, the sample type will be changed and a cancer gene panel test that is covered by insurance will be performed.

[0137] By implementing the support system of the present invention in society using this model, it will be possible to make the most of the opportunity for cancer gene panel testing, which is covered by insurance and can only be received once in a lifetime. [Industrial Applicability]

[0138] The present invention can be applied to an analysis pipeline in cancer gene panel testing. [Explanation of symbols]

[0139] 0 Support System NW communication network 1. Information processing equipment 101 Processing section 102 Storage section 103 Communications Department 104 Input section 105 Output section 110 Base Calling Methods 111 Quality Check Methods 112 Quality Standard Change Acceptance Method 113 Alignment Means 114 Variant calling methods 115 Variant calling condition change acceptance means 116 Search Processing Means 117 Annotation Methods 118 Gene Mutation Assessment Tools 119 List data creation method 120 Extraction / Sorting Methods 121 Gene Mutation Assessment Tools 121 Reporting Methods 122 External information acquisition means 123 Database Update Methods 124 Condition setting acceptance means 125 Table data output means 2. Base sequence analyzer 3 External Server 31 External Databases 311 Internal Database 4 User terminal equipment

Claims

1. A therapeutic drug selection and / or clinical trial entry decision support system that supports the selection of a therapeutic drug or the decision on whether to enter a clinical trial based on the analysis results of a base sequence analyzer of a sample collected from a subject of a cancer gene panel test; The system comprises a search processing means; The search processing means Refer to a database in which clinical information, including information on therapeutic drugs or clinical trials, and information on specific gene mutations to which the therapeutic drugs or clinical trials are applicable are linked and recorded; Among the genetic mutations called as variants by analyzing the data output from the base sequence analyzer, is identical to the specific genetic mutation; or A therapeutic drug selection and / or clinical trial entry decision support system that searches for and identifies gene mutations that are presumed to have the same effect on the function of the gene product.

2. The system according to claim 1 , further comprising an annotation means for linking the genetic mutation identified by the search processing means to the clinical information and recording it.

3. the system comprising a base calling means, a quality checking means, an alignment means, and a variant calling means; the base calling means calls base sequence information based on the data primarily acquired by the base sequence analyzing device; The quality check means checks a predetermined quality standard among the reads included in the base sequence information called by the base call means; the alignment means aligns the base sequence information called by the base calling means to a reference sequence; The system according to claim 1 , wherein the variant calling means calls genetic variations in accordance with predetermined variant calling conditions based on differences between the base sequence information and the reference sequence identified by alignment by the alignment means.

4. The system includes a means for accepting changes to quality criteria and / or a means for accepting changes to variant calling conditions; the quality standard change accepting means accepts a change to the quality standard; The therapeutic drug selection and / or clinical trial entry decision support system according to claim 3 , wherein the variant calling condition change acceptance means accepts changes to the variant calling conditions.

5. The system includes a communication unit, the database is an external database recorded in an external device, The search processing means is configured to perform a reference to the external database by communicating with the external device through the communication unit. The therapeutic drug selection and / or clinical trial entry decision support system according to claim 1.

6. The system includes a communication unit, a storage unit, an external information acquisition unit, and a database update unit; the database is an internal database recorded in the storage unit; the external information acquisition means acquires information of an external database recorded in an external device through the communication unit and records the information in the storage unit; a database update means for updating the internal database by reflecting the information acquired by the external information acquisition means; The external database is a database in which clinical information including information on therapeutic drugs or clinical trials and information on specific gene mutations applicable to the therapeutic drugs or clinical trials are linked and recorded. The therapeutic drug selection and / or clinical trial entry decision support system according to claim 1.

7. The system comprises a genetic mutation evaluation means; the genetic mutation evaluation means evaluates whether the genetic mutation called by the variant calling means is a somatic mutation or a germline mutation; The therapeutic drug selection and / or clinical trial entry decision support system according to claim 1.

8. The system comprises a list data creating means and an extracting / sorting means; the list data creation means creates list data of variant-called gene variations; The therapeutic drug selection and / or clinical trial entry decision support system according to claim 1, wherein the extraction / sorting means has the function of performing one or more of the processes specified in the following (A) to (C) on the list data. (A) Of the variant-called genetic variations, only those genetic variations identified by the search processing means are extracted. (B) Among the variant-called genetic variations, genetic variations identified by the search processing means are sorted so as to be placed at the top of the list data. (C) Among the variant-called gene mutations, those for which one or more analysis quality parameters obtained by base sequence analysis using the base sequence analyzer satisfy specific conditions are extracted.

9. A condition setting means is provided; The condition setting means receives an input operation by a user, The type of the analysis quality parameter to be extracted in (C), and / or The specific conditions regarding the analysis quality parameters, which are the extraction criteria in (C). The therapeutic drug selection and / or clinical trial entry decision support system according to claim 8, wherein the following is set:

10. A method for supporting the selection of a therapeutic drug and / or the decision to enter a clinical trial, which supports the selection of a therapeutic drug or the decision to enter a clinical trial based on the analysis results of a sample collected from a subject of a cancer gene panel test by a base sequence analyzer; The method comprises a computer-implemented search processing step; In the search processing step, Refer to a database in which clinical information, including information on therapeutic drugs or clinical trials, and information on specific gene mutations applicable to the therapeutic drugs or clinical trials are linked and recorded; Among the genetic mutations called as variants by analyzing the data output from the base sequence analyzer, is identical to the specific genetic mutation; or A method for supporting the selection of therapeutic drugs and / or clinical trial entry decisions, which searches for and identifies gene mutations that are presumed to have the same effect on the function of the gene product.

11. A therapeutic drug selection and / or clinical trial entry decision support program that supports the selection of a therapeutic drug or the decision on whether to enter a clinical trial based on the analysis results of a base sequence analyzer of a sample collected from a subject of a cancer gene panel test; The program causes a computer to function as a search processing means; The search processing means Refer to a database in which clinical information, including information on therapeutic drugs or clinical trials, and information on specific gene mutations applicable to the therapeutic drugs or clinical trials are linked and recorded; Among the genetic mutations called as variants by analyzing the data output from the base sequence analyzer, is identical to the specific genetic mutation; or A program to support therapeutic drug selection and / or clinical trial entry decisions by searching for and identifying gene mutations that are presumed to have the same effect on the function of the gene product.

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