KR20230039167A - Discovery method for therapeutic target gene based on membrane protein and analysis apparatus - Google Patents

Abstract

The method for discovering a therapeutic target gene by using membrane protein information comprises steps in which an analysis apparatus: receives information about tumor cell-specific membrane protein encoding genes for target tumors, and dependent genes of the tumor cells; obtains genomic data analysis information about a sample; generates combination gene sets that can be formed from the membrane protein genes and the dependent genes; identifies candidate combination gene sets expressed in tumor cells of the sample from among the combination gene sets; and discovers a final therapeutic target gene from among candidate combination gene sets on the basis of normal cell coverage and tumor cell coverage for each of the candidate combination gene sets. Therefore, an effective therapeutic target gene may be discovered.

Description

Method and analysis device for discovering therapeutic target gene using membrane protein information

The technique described below relates to a technique for discovering a target gene for tumor treatment.

Tumor dependency refers to a phenomenon in which tumor cells depend on a specific gene essential for tumor cell proliferation and survival, or a gene corresponding to this concept. When the expression of a tumor-dependent gene is suppressed, the growth of tumor tissue is reduced, and as a result, the death of tumor cells can be induced. Thus, tumor-dependent genes can be used as therapeutic targets for tumor treatment.

Korean Patent Publication No. 10-2018-0092395

An important issue in discovering therapeutic target genes for tumor treatment is that only tumor cells should be targeted. This is because if the chiro target affects normal cells, the treatment itself can cause great side effects to the patient.

The technology described below aims to provide a technique for discovering effective therapeutic target genes for tumor cells using membrane protein information.

A method of discovering a target gene for treatment using membrane protein information includes the step of receiving, by an analysis device, information on specific membrane protein-encoding genes of tumor cells for a target tumor and dependent genes of the tumor cells; Acquiring genome data analysis information, generating, by the analysis device, combinatorial gene sets in which the membrane protein genes and the dependent genes are configurable, the analysis device expressing expression in the tumor cells of the sample among the combinatorial gene sets identifying candidate combinatorial gene sets, and the analysis device discovering a final therapeutic target gene from among the candidate combinatorial gene sets based on normal cell coverage and tumor cell coverage for each of the candidate combinatorial gene sets. .

An analysis device for discovering a target gene for treatment includes an input device for receiving genomic data of a sample, a storage device for storing information on specific membrane protein-encoding genes of tumor cells for a target tumor and dependent genes of the tumor cells, and the above Genome data is processed to generate cell type information and cell-specific gene expression information for the sample, combinatorial gene sets in which the membrane protein genes and the dependent genes are configurable are generated, and among the combined gene sets, the sample and an arithmetic unit for identifying candidate combinatorial gene sets expressed in tumor cells of the candidate combinatorial gene set and discovering a final treatment target gene based on normal cell coverage and tumor cell coverage among the candidate combinatorial gene sets.

The technology described below can discover an effective target gene capable of targeting tumor cells while minimizing the effect on normal cells by using tumor cell-specific membrane protein information.

1 is a result of correlation analysis between the expression level of a gene and the dependence of tumor cells.
2 is a graph showing the correlation between expression heterogeneity and dependence of genes in tumor cells.
3 is an example of a graph showing the degree of gene dependence for mpEAD gene combination candidates.
4 is a result showing the coverage by cell type for the TM4SF4_KRT8 gene combination.
5 is a result showing coverage by cell type for mpEAD combinatorial genes selected as final candidates.
6 is a result showing the coverage of cells expressing KRT8 or ATP5MC1 while having ASGR1 as a membrane protein for liver cancer.
7 is a result showing coverage of cells expressing KLF5 or ERBB3 while having MSLN as a membrane protein for pancreatic cancer.
8 is an example of a system for discovering a target gene for treatment.
9 is an example of a process of finding a target gene for treatment.
10 is an example of an analysis device for discovering a target gene for treatment.

Since the technology to be described below can have various changes and various embodiments, specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the technology described below to specific embodiments, and it should be understood to include all modifications, equivalents, or substitutes included in the spirit and scope of the technology described below.

Terms such as first, second, A, B, etc. may be used to describe various elements, but the elements are not limited by the above terms, and are merely used to distinguish one element from another. used only as For example, without departing from the scope of the technology described below, a first element may be referred to as a second element, and similarly, the second element may be referred to as a first element. The terms and/or include any combination of a plurality of related recited items or any of a plurality of related recited items.

In terms used in this specification, singular expressions should be understood to include plural expressions unless clearly interpreted differently in context, and terms such as “comprising” refer to the described features, numbers, steps, operations, and components. , parts or combinations thereof, but it should be understood that it does not exclude the possibility of the presence or addition of one or more other features or numbers, step-action components, parts or combinations thereof.

Prior to a detailed description of the drawings, it is to be clarified that the classification of components in the present specification is merely a classification for each main function in charge of each component. That is, two or more components to be described below may be combined into one component, or one component may be divided into two or more for each more subdivided function. In addition, each component to be described below may additionally perform some or all of the functions of other components in addition to its main function, and some of the main functions of each component may be performed by other components. Of course, it may be dedicated and performed by .

In addition, in performing a method or method of operation, each process constituting the method may occur in a different order from the specified order unless a specific order is clearly described in context. That is, each process may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

The technique described below is a technique for selecting a target gene for tumor treatment based on gene expression data in cells of a sample (tissue). First, the data used by the researcher and the process of selecting the target gene will be described.

The depmap portal is a database created by MIT's Broad Institute and Harvard to systematically identify genetic and pharmacological tumor dependencies. The Dmap portal contains large-scale experimental data from loss-of-function screens for various tumor cell lines. The investigators used loss-of-function screen data for 789 tumor cell lines in this experimental data. The data included tumor cell lines derived from 28 different tumors, including liver, pancreas, colon and lung.

The researcher analyzed the correlation between expression level and dependence for 18,121 genes included in the data imported from the Dmap portal. 1 is a result of correlation analysis between the expression level of a gene and the dependence of tumor cells. In the graph of FIG. 1, the horizontal axis is the correlation R value, and the vertical axis is the logarithmic value of the p value. 1,234 genes (p<0.05) showed a statistically significant negative correlation in which the absolute value of the dependence value increased as the expression level increased. The larger the negative absolute value of the dependence value, the more important the corresponding gene is for the proliferation of tumor cells.

The researcher named 1,234 statistically significant genes as EAD (Expression-Associated Dependency) genes among the genes for which the degree of dependence of tumor cells on the gene increases as the expression level increases. As a result of this study, the gene showing the strongest negative correlation was the KLF5 gene.

Expression heterogeneity is a value that quantifies how uniformly the expression value of a gene is expressed across multiple cells. Expression heterogeneity means that the lower the number, the more cells express the corresponding gene to a similar degree.

2 is a graph showing the correlation between expression heterogeneity and dependence of genes in tumor cells. In FIG. 2, the horizontal axis is the value of expression heterogeneity, and the vertical axis is the value of the fitness effect. 2(A) shows the correlation between expression heterogeneity and dependence of genes targeting 15,000 genes using single cell RNA sequencing data and gene dependence data of liver cancer. 2(B) shows the correlation between expression heterogeneity and dependence of genes targeting 15,000 genes using single cell RNA sequencing data and gene dependence data of pancreatic cancer. In FIG. 2, one dot represents one gene, and the degree of dependency increases as the expression heterogeneity of the gene decreases. This can be interpreted as the fact that as the degree of dependence of tumor tissue on a specific gene increases, many tumor cells uniformly express the corresponding gene.

Combining expression heterogeneity analysis and the EAD concept, EAD genes that are highly expressed in the entire tumor tissue also increase in proportion to the degree to which the tumor tissue depends on the corresponding gene. As such, in the case of a gene highly dependent on tumor tissue, expression heterogeneity is low, and various tumor cells included in tumor tissue tend to express the corresponding gene uniformly. Therefore, if a specific tumor tissue or tumor cell line highly expresses EAD genes, a situation in which the tumor cells proliferate highly dependent on the corresponding gene and at the same time express the corresponding gene highly and uniformly can be expected. From the perspective of genetic difference treatment, the EAD gene, which is highly expressed in tumor tissue, can be targeted for down regulation. Since the corresponding EAD gene is highly likely to be highly and uniformly expressed in most tumor cells, downregulation of the corresponding gene may inhibit tumor tissue proliferation or induce apoptosis. Hereinafter, EAD genes to be regulated for tumor treatment are referred to as therapeutic target genes or target EAD genes.

According to the above concept, the researcher tried to find EAD genes highly expressed in the tumor tissue of interest. To this end, the researchers used a published single cell RNA sequence (scRNA-seq) data set. The dataset consists of information from 15 and 16 patients, respectively, for liver cancer and pancreatic cancer. The data set includes gene expression information for 34,723 and 33,583 single cells that passed certain filtering criteria for liver cancer and pancreatic cancer, respectively.

scRNA-seq data can find target EAD genes only for pure malignant cells, excluding normal cells (non-malignant cells). In addition, scRNA-seq data has the advantage of being able to quantitatively confirm how many cells by cell type expressed the corresponding EAD target.

Researchers first process data with scRNA-seq in the form of FASTQ to create a count matrix, and then use gene expression profiles to perform quality control, clustering analysis, and cell type annotation. annotation) and aneuploid prediction were performed in order.

Through this procedure, the researcher was able to select only aneuploid cells among the cells annotated as epithelial cells in the tumor tissue, and classified these cells as malignant cells. Other cells were classified as normal cells.

The proportion of cells expressing a specific EAD gene in the two cell groups can be calculated through a count matrix. The EAD gene that researchers are looking for is a gene that can target many malignant cells while targeting few normal cells. A target EAD gene can be selected based on the coverage of each group.

However, the problem with this method is that when a treatment target gene is found through the above method, a large number of target genes with high normal cell coverage as well as tumor coverage may occur. That is, regulation of the target gene may affect not only tumor cells but also normal cells, resulting in unwanted side effects. This action is called the off-tumor effect.

In order to find a target EAD gene that dramatically lowers normal coverage while maintaining high tumor coverage, the researcher detected a target gene for treatment by combining membrane protein and EAD.

The process described below corresponds to an in silico process using a computer device. Of course, the target EAD gene may be derived in the same process using a data set obtained through an in vitro experiment process.

The researcher used a method of filtering out normal cells based on membrane proteins specifically expressed in tumor cells and searching for the EAD gene in the remaining tumor cells. This process may be performed sequentially or concurrently.

The researcher combined the 1,234 EAD genes mentioned above with 2,802 surfaceome genes to create a mpEAD combination gene list. Among them, the researcher selected 81 and 627 optimal mpEAD combination genes for liver cancer and pancreatic cancer, respectively, with tumor coverage as high as 60% and normal coverage as low as 10% or less. Table 1 below shows some examples of mpEAD combination genes for liver cancer, and Table 2 below shows some examples of mpEAD combination genes for pancreatic cancer.

Tables 1 and 2 are examples of 20 genes with the lowest normal cell coverage among mpEAD combination genes selected from liver cancer and pancreatic cancer. The mpEAD combination gene is a gene belonging to a membrane protein on the left, and a gene belonging to an EAD gene on the right.

For liver cancer, the TM4SF4_CFHR1 combination showed the second lowest normal coverage at 0.42%. Tumor coverage of the TM4SF4_CFHR1 combination is about 76%. Therefore, if the gene combination is used as a therapeutic target, it can be an effective target with minimized off-tumor effects.

Including the above 20 combinations, the researcher selects two combinations that are highly likely to be used effectively in actual clinical practice by referring to information published in papers, etc. did Table 3 below shows the finally selected mpEAD combination genes. For liver cancer, ASGR1_KRT8 and ASGR1_ATP5MC1 were selected, and for pancreatic cancer, MSLN_KLF5 and MSLN_ERBB3 were selected.

In order to select a combination that can be used clinically among the combinations found through the mpEAD combination discovery pipeline, the researcher first confirmed the dependency score of the EAD genes. To this end, the researcher confirmed the dependency scores of the EAD genes included in the final combination candidate for 789 tumor cell lines included in the aforementioned DMAP data.

3 is an example of a graph showing the degree of gene dependence for mpEAD gene combination candidates. FIG. 3(A) shows the degree of gene dependence of liver cancer (HCC) on the mpEAD gene combination, and FIG. 3 (B) shows the degree of gene dependence of pancreatic cancer (PDAC) on the mpEAD gene combination. In FIG. 3 , red dots represent corresponding cancers, and black dots represent other cancers.

In FIG. 3 , the y-axis indicates the degree of dependence of the gene effect value, and a value of 0 indicates that the gene is not essential in the corresponding tumor cell lines. In addition, a gene effect value lower than 0 means that the corresponding gene is essential in the corresponding tumor vertical lines. Referring to FIG. 3 , it can be seen that the mpEAD combination genes belonging to the final combination candidates are essential in many types of tumor cell lines. In addition, referring to FIG. 3 , it can be seen that cell lines (liver cancer or pancreatic cancer) of the target tumor show a pattern similar to the overall distribution.

Next, the researcher examined the association between cancer types and the corresponding genes through a literature search targeting the membrane genes included in the final candidates. According to the results of previous studies, ASGR1 is a gene specifically expressed only in liver tissue and has attracted attention as a mediator for the target delivery of anticancer drugs. In addition, MSLN has been reported to be expressed in most pancreatic cancers and is not expressed in normal tissues.

Finally, the researcher divided the normal cells by cell type and confirmed the target coverage for each normal cell type. This is because coverage for a specific cell type may be dangerously high even though total normal cell coverage is low.

For example, TM4SF4_KRT8, one of the mpEAD combination genes discovered in liver cancer, had high tumor coverage of 79.93% and low normal coverage of 3.19%.

4 is a result showing the coverage by cell type for the TM4SF4_KRT8 gene combination. TM4SF4_KRT8 showed higher coverage of epithelial cells than weak tumor cells in normal coverage analysis by cell type. In other words, TM4SF4_KRT8 is an effective mpEAD combination gene candidate when analyzed for all normal cells and malignant tumor cells, but it can be said to be an inappropriate target when examining coverage by individual cell type. This is because regulating gene expression by targeting TM4SF4_KRT8 is likely to have a greater effect on epithelial cells than on tumor cells.

Criteria are needed to filter mpEAD combinatorial gene candidates while analyzing coverage by cell type. A variety of criteria may be used. For example, (1) when the tumor coverage of the mpEAD combinatorial gene candidate is greater than the first threshold value and the coverage of all normal individual cells is less than the second threshold value, the final candidate combinatorial gene (treatment target gene) may be selected. (2) Alternatively, when the tumor coverage of the mpEAD combinatorial gene candidate is equal to or greater than the first threshold and the coverage of all individual normal cells is lower than the tumor coverage, the final candidate combinatorial gene may be selected. (3) Alternatively, when the tumor coverage of the mpEAD combinatorial gene candidate is equal to or greater than the first threshold and the tumor coverage of all individual normal cells is lower than the tumor coverage by a predetermined value or more, the final candidate combinatorial gene may be selected. (4) Alternatively, when the tumor coverage of the mpEAD combinatorial gene candidate is equal to or greater than the first threshold, and the average coverage of the top few normal cells with high coverage is lower than the tumor coverage by a predetermined value or more, it can be selected as the final candidate combinatorial gene.

5 is a result showing coverage by cell type for mpEAD combinatorial genes selected as final candidates. 5, it can be seen that (i) ASGR1_KRT8 and ASGR1_ATP5MC1 for liver cancer (HCC) and (ii) MSLN_KLF5 and MSLN_ERBB3 for pancreatic cancer (PDAC) have sufficiently low coverage in normal cells other than tumor cells. Therefore, the mpEAD combination genes finally selected by the researcher can be said to be genes capable of targeting multiple tumor cells while minimizing the off-tumor effect.

Meanwhile, the two mpEAD combination genes selected from each of liver cancer and pancreatic cancer have the same membrane protein. Therefore, targeting cells having at least one of the two EAD genes among cells having the corresponding membrane protein is expected to have more improved tumor coverage.

6 is a result showing the coverage of cells expressing KRT8 or ATP5MC1 while having ASGR1 as a membrane protein for liver cancer. 7 is a result showing coverage of cells expressing KLF5 or ERBB3 while having MSLN as a membrane protein for pancreatic cancer. Referring to FIG. 6, the tumor cell coverage increased by about 7% compared to the existing combination, and the normal cell coverage was about 2.7%. Referring to FIG. 7, the tumor cell coverage increased by about 7% compared to the existing combination, and the normal cell coverage was about 2% in pancreatic cancer. Therefore, for the combination genes possessed by membrane proteins among the finally selected mpEAD combination genes, a method of targeting cells having any one of the EAD genes is expected to have a great gene therapy effect.

Based on the above-mentioned research results, the process of finding genes to be targeted for genetic therapy will be summarized and explained. The process described below is performed by the analysis device. The analysis device corresponds to a computer device capable of processing data. For example, the analysis device may be implemented as a PC, a server on a network, a smart device, a chipset in which a dedicated program is embedded, and the like.

The required data set is gene expression data in the cells of the sample composition. Meanwhile, the analysis device may identify tumor-specific membrane proteins based on data obtained by screening tumor cell proteins rather than gene expression data. However, it is assumed that the following analysis device identifies cells having tumor-specific membrane proteins using gene expression data.

8 is an example of a system 100 for discovering a target gene for treatment. In FIG. 8 , analysis devices 130 , 140 , and 150 discover therapeutic target genes. In FIG. 8, the analysis device is shown in the form of a server 130 and computer terminals 140 and 150.

The system 100 for discovering a therapeutic target gene discovers a therapeutic target gene for a target carcinoma. The target carcinoma may be any one of various cancers such as liver cancer, lung cancer, pancreatic cancer, and breast cancer.

The genome analysis device 110 generates genetic data by analyzing a sample of a patient having a target carcinoma disease. Gene data is data that can know the expression level of a gene. Genetic data may be data on DNA or RNA sequences. The genome analysis device 110 may generate genetic data for a specific patient or patient group having a specific disease. In the case of data for a specific patient, the analysis devices 130, 140, and 150 may discover target genes for personalized treatment.

The gene database 120 holds tumor-specific membrane protein information and EAD gene information for a target carcinoma. Tumor-specific membrane protein information includes information of a gene encoding the protein. As described above, the EAD gene refers to a gene whose dependence on a target carcinoma increases as the expression level of the gene increases.

The server 130 receives genetic data for a sample from the genome analysis device 110 . The server 130 also receives the type of target cancer type. The server 130 queries the type of target carcinoma and receives membrane protein information and EAD gene information for the target carcinoma from the gene DB 120 . The server 130 discovers a gene that is a target for treatment in a sample based on membrane protein information (coding gene) and EAD gene information received from the gene DB 120 . The user 10 may access the server 130 through a user terminal (PC, smart phone, etc.) and check the analysis result performed by the server 130.

The computer terminal 140 receives genetic data for a sample from the genome analysis device 110 . The computer terminal 140 also receives a type of target cancer type. The computer terminal 140 queries the type of target carcinoma and receives membrane protein information and EAD gene information for the target carcinoma from the gene DB 120 . The computer terminal 140 discovers a target gene for treatment in a sample based on membrane protein information (coding gene) and EAD gene information received from the gene DB 120 . The user 20 may check the analysis result through the computer terminal 140 used by the user 20 .

The computer terminal 150 receives genetic data through a medium (eg, USB, SD card, etc.) in which the genetic data for the sample generated by the genome analysis device 110 is stored. The computer terminal 150 also receives the type of target cancer type. The computer terminal 150 queries the type of target carcinoma and receives membrane protein information and EAD gene information for the target carcinoma from the gene DB 120 . The computer terminal 150 discovers a target gene for treatment in a sample based on membrane protein information (coding gene) and EAD gene information received from the gene DB 120 . The user 30 may check the analysis result through the computer terminal 150 used by the user 30 .

9 is an example of a process 200 for finding a target gene for treatment.

The analyzer acquires tumor-specific membrane protein coding gene and EAD gene information for the target carcinoma (210). The analysis device may obtain information on multiple types of carcinoma. The analysis device may receive tumor-specific membrane protein coding gene and EAD gene information from a network connected DB. In addition, the analysis device may receive tumor-specific membrane protein coding gene and EAD gene information from a physically connected storage medium.

The analysis device acquires the genome data of the sample and performs a predetermined pre-processing process (220). The analysis device may receive genome raw data (FASTQ) of the sample and generate data on genome sequence sequencing and expression level using its own program. The analyzer may generate cell type annotation (normal cells or malignant cells) for cells, count matrix generation for cells, gene expression information in each cell, and the like.

The analyzer may generate the above-described mpEAD combinatorial gene list (230). The mpEAD combinatorial gene list consists of gene sets in which (i) tumor cell-specific membrane proteins (coding genes) and (ii) EAD genes are one set. The mpEAD combinatorial gene list includes all possible combinations of tumor-specific membrane protein coding genes and EAD genes.

The analyzer identifies expression in the sample for each set of genes in the mpEAD combinatorial gene list (240). The analysis device selects candidate combinatorial gene(s) based on normal cell coverage and tumor cell coverage with respect to the mpEAD combinatorial gene set(s) expressed in the sample (250). The analysis device may select, as a final candidate combination gene, a gene whose coverage of all tumor cells is higher than the first threshold value and at the same time, the coverage of normal cells is lower than the second threshold value, among the mpEAD combination genes whose expression has been confirmed. The first threshold value and the second threshold value may be set in advance according to experimental results. Furthermore, the threshold value may be constantly adjusted based on the result analyzed in the sample. For reference, the researcher conducted the experiment by setting the first threshold value = 60% and the second threshold value = 10%. Using this method, the analysis device filters out normal cells expressing a specific EAD gene.

Furthermore, the analysis device may check coverage by cell type for each of the candidate combinatorial genes (260). As discussed above, candidate combinatorial genes may have higher coverage in certain types of normal cells than in tumor cells. In order to eliminate such a case, the analysis device may select a final treatment target gene based on coverage for each cell type (270). When the coverage for individual normal cells is lower than the coverage for tumor cells, the analysis device may select a candidate combination gene as a treatment target gene. Specific criteria for selecting a therapeutic target gene may vary. For example, (1) when the tumor coverage of the mpEAD combination gene candidate is equal to or greater than the first threshold value and the coverage of all normal individual cells is equal to or less than the second threshold value, the gene candidate may be selected as the final treatment target gene. (2) Alternatively, when the tumor coverage of the mpEAD combinatorial gene candidate is equal to or greater than the first threshold and the coverage of all individual normal cells is lower than the tumor coverage, the candidate may be selected as the final treatment target gene. (3) Alternatively, when the tumor coverage of the mpEAD combinatorial gene candidate is equal to or higher than the first threshold value and the coverage of all individual normal cells is lower than the tumor coverage by a predetermined value or more, it may be selected as the final treatment target gene. (4) Alternatively, when the tumor coverage of the mpEAD combinatorial gene candidate is higher than the first threshold and the average coverage of the top few normal cells with high coverage is lower than the tumor coverage by a certain value or more, it can be selected as the final treatment target gene.

10 is an example of an analysis device 300 for discovering a target gene for treatment. The analysis device 300 is a device corresponding to the analysis device 130 , 140 or 150 of FIG. 1 . The analysis device 300 may be physically implemented in various forms. For example, the analysis device 300 may have a form of a PC, a smart device, a server on a network, or a chipset dedicated to data processing.

The analysis device 300 may include a storage device 310, a memory 320, an arithmetic device 330, an interface device 340, a communication device 350, and an output device 360.

The storage device 310 may store programs or source codes necessary for data processing.

The storage device 310 may retain tumor cell-specific membrane protein information (coding gene) and EAD gene information for a specific tumor.

The storage device 310 may store genome analysis data of a sample. The genome analysis data of the sample may include gene expression information, cell type, gene expression for each cell, and the like.

The storage device 310 may store treatment target gene information as an analysis result.

The memory 320 may store data and information generated in the course of the analysis device 300 analyzing a target gene for treatment of a specific tumor.

The interface device 340 is a device that receives certain commands and data from the outside.

The interface device 340 may receive tumor cell-specific membrane protein information (coding gene) and EAD gene information for a specific tumor.

The interface device 340 may receive genetic data of a sample (patient) from a physically connected input device or an external storage device. The interface device 340 may receive an input of the type of target tumor.

The communication device 350 refers to a component that receives and transmits certain information through a wired or wireless network.

The communication device 350 may receive tumor cell-specific membrane protein information (coding gene) and EAD gene information for a specific tumor from an external object.

The communication device 350 may receive genetic data of a sample (patient). The communication device 350 may receive the type of target tumor.

The communication device 350 may transmit an analysis result of the sample to an external object.

The communication device 350 or interface device 340 is a device that receives certain data or commands from the outside. The communication device 350 or interface device 340 may be referred to as an input/output device. In addition, considering only information input capability, the communication device 350 or the interface device 340 may be referred to as an input device.

The output device 360 is a device that outputs certain information. The output device 360 may output interfaces and analysis results necessary for data processing.

The data processing and analysis process performed by the arithmetic device 330 is the same as the process described with reference to FIG. 9 .

The computing device 330 may process the genomic data of the sample. As described above, the computing device 330 may generate cell type annotation (normal cells or malignant cells), generation of a count matrix for cells, gene expression information in each cell, and the like.

The computing device 330 may generate a mpEAD combinatorial gene list for a target carcinoma.

The computing device 330 may select candidate combinatorial genes based on the presence or absence of membrane proteins and EAD genes expressed in the sample.

The computing device 330 may select final candidate combinatorial gene(s) based on normal cell coverage and tumor cell coverage for each of the candidate combinatorial genes.

The computing device 330 may select a final treatment target gene based on the coverage for each cell type of each of the final candidate fusion genes among the final candidate fusion genes.

The arithmetic device 330 may be a device such as a processor, an AP, or a chip in which a program is embedded that processes data and performs certain arithmetic operations.

In addition, the above-described method for discovering a target gene for treatment of a specific tumor may be implemented as a program (or application) including an executable algorithm that may be executed on a computer. The program may be stored and provided in a temporary or non-transitory computer readable medium.

A non-transitory readable medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and can be read by a device. Specifically, the various applications or programs described above are CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM (read-only memory), PROM (programmable read only memory), EPROM (Erasable PROM, EPROM) Alternatively, it may be stored and provided in a non-transitory readable medium such as EEPROM (Electrically EPROM) or flash memory.

Temporary readable media include static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), and enhanced SDRAM (Enhanced SDRAM). SDRAM, ESDRAM), Synchronous DRAM (Synclink DRAM, SLDRAM) and Direct Rambus RAM (DRRAM).

This embodiment and the drawings accompanying this specification clearly represent only a part of the technical idea included in the foregoing technology, and those skilled in the art can easily understand it within the scope of the technical idea included in the specification and drawings of the above technology. It will be obvious that all variations and specific examples that can be inferred are included in the scope of the above-described technology.

Claims (9)

receiving, by an analysis device, information about specific membrane protein-encoding genes of tumor cells for a target tumor and dependent genes of the tumor cells;
obtaining, by the analysis device, genome data analysis information of the sample;
generating, by the analysis device, combinatorial gene sets in which the membrane protein genes and the dependent genes are configurable;
identifying, by the analysis device, candidate combinatorial gene sets expressed in tumor cells of the sample from among the combinatorial gene sets; and
Discovering, by the analysis device, a final treatment target gene among candidate combinatorial gene sets based on normal cell coverage and tumor cell coverage for each of the candidate combinatorial gene sets,
The dependent gene is a gene having a correlation between the expression level and dependency of a gene of a threshold value or higher among genes of the tumor cell, and the method of discovering a target gene for treatment using membrane protein information. According to claim 1,
The genome data analysis information is a method of discovering a therapeutic target gene using membrane protein information including cell type information and cell-specific gene expression information. According to claim 1,
The analysis device discovers, among the candidate combinatorial gene sets, a set in which the tumor cell coverage is greater than or equal to the first threshold and the normal cell coverage is less than the second threshold as the final treatment target gene. Treatment target gene using membrane protein information excavation method. According to claim 1,
The step of discovering the final therapeutic target gene is
selecting, by the analysis device, a set in which the tumor cell coverage is greater than or equal to a first threshold value and the normal cell coverage is less than a second threshold value among the candidate combinatorial gene sets as a final candidate combinatorial gene set; and
The method of discovering, by the analysis device, the final therapeutic target gene based on coverage for each cell type among the final candidate combinatorial gene set. According to claim 4,
A treatment target using membrane protein information in which the analyzer discovers, as the final treatment target gene, a final candidate gene set whose coverage for normal cells, which has the highest coverage among the set of final candidate combinatorial genes, is lower than the coverage for tumor cells by a reference value or more. Gene discovery method. an input device that receives the genome data of the sample;
a storage device for storing information about specific membrane protein coding genes of tumor cells for a target tumor and dependent genes of the tumor cells; and
The genomic data is processed to generate cell type information and cell-specific gene expression information for the sample, to generate combinatorial gene sets in which the membrane protein genes and the dependent genes are configurable, and among the combined gene sets A calculation device for identifying candidate combinatorial gene sets expressed in tumor cells of the sample and discovering a final treatment target gene based on normal cell coverage and tumor cell coverage among the candidate combinatorial gene sets,
The dependent gene is an analysis device for discovering a therapeutic target gene, wherein the expression level and dependency of the gene among the genes of the tumor cells have a correlation of a threshold value or higher. According to claim 6.
Wherein the calculation device discovers, as a final treatment target gene, a set in which the tumor cell coverage is greater than or equal to a first threshold value and the normal cell coverage is less than a second threshold value among the candidate combinatorial gene sets. According to claim 6.
The computing device selects, as a final candidate combinatorial gene set, a set in which the tumor cell coverage is greater than or equal to a first threshold value and the normal cell coverage is less than a second threshold value among the candidate combinatorial gene sets, and the final candidate combinatorial gene set An analysis device for discovering a therapeutic target gene for discovering the final therapeutic target gene based on coverage for each cell type among the cells. According to claim 8.
The calculation device is an analysis device for discovering, as the final treatment target gene, a final candidate gene set whose coverage for normal cells, which has the highest coverage among the set of final candidate combinatorial genes, is lower than the coverage for tumor cells by a reference value or more. .

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