KR20210015373A - Method and system for predicting sensitizer for overcoming cancer drug resistance - Google Patents

Abstract

The present invention relates to a method for extracting a patient′s resistance-related gene and resistance mechanism based on a large scale molecular network and patient′s transcript data by using an in silico (computer model) method rather than conventional in vivo and in vitro methods, and predicting a sensitizer having an optimal anticancer effect that effectively acts on the extracted resistance mechanism to return to a state before the development of resistance. When the method of the present invention is used, a sensitizer having an optimal anticancer effect for enabling a body to overcome resistance to an anticancer agent and sensitively react to the anticancer agent again can be selected, and thus, the present invention can be effectively utilized for customized treatment according to the advent of the age of precision medicine after undergoing experimental verification.

Description

A computational method for predicting a sensitizer for overcoming the resistance of an anticancer drug, and a system therefor {Method and system for predicting sensitizer for overcoming cancer drug resistance}

The present invention relates to a method of predicting a sensitizer for overcoming cancer drug resistance (discovery of sensitizers to overcome cancer drug resistance, SOCAR), and in particular, a patient's potential resistance gene using a large-scale molecular network and patient's transcriptome data. It relates to a method of extracting and resistance mechanisms, and predicting an optimal sensitizer.

Cancer is known to be caused by abnormal proliferation and growth of cells due to various causes. These causes include exposure to chemical carcinogens, infection with carcinogens and congenital genetic abnormalities. But basically, all of these causes are the ones that cause abnormalities in the genes in the cell. In normal cells, the functions of oncogene, tumor suppressor gene, and apoptosis regulating gene are complementarily regulated to grow and maintain harmoniously. Cancer genes are normally essential genes for cell growth and differentiation. They contribute to cell proliferation and growth by promoting protein synthesis and transducing intracellular signals, but when mutations occur, they induce excessive cell proliferation and cause cancer. It is known to contribute. Cancer is the world's second highest cause of death after circulatory system disease, and research for cancer treatment has been extensively conducted, and cancer treatment methods known to date include surgery, radiation therapy, chemotherapy, immunotherapy, and gene therapy. Etc.

Anticancer agents used in chemotherapy are drugs that inhibit the growth or proliferation of cancer cells. It is largely classified as a cytotoxic anticancer agent, a target anticancer agent, and an immune anticancer agent, and a combination chemotherapy that uses two or more drugs at the same time is used to select an anticancer agent according to the type, progression, and condition of the patient, etc. . Cytotoxic anticancer agents, known as first-generation anticancer agents, have anticancer effects by directly attacking indiscriminate and rapidly differentiating cells. However, since normal cells with fast differentiation characteristics (e.g., hematopoietic stem cells, hair root cells, intestinal mucosa cells, etc.) of the bone marrow are also attacked by cytotoxic anticancer drugs, they cause side effects such as leukocyte reduction, hair loss, vomiting, and diarrhea There is this. Targeted anticancer agents, known as second-generation anticancer agents, block signal transduction involved in cancer growth and differentiation by acting on specific proteins or specific gene changes that appear in cancer cells. Unlike cytotoxic anticancer drugs, it does not act on normal cells, but acts specifically on cancer cells, so it has few side effects.

However, one of the barriers to the use of chemotherapy is that it develops resistance to cancer drugs. In order to succeed in chemotherapy for cancer patients, cancer cells must be killed at a blood concentration where normal tissues can survive, and drug resistance to anticancer drugs is administered with an anticancer drug in an amount that can reach the blood concentration that can kill cancer cells. It refers to the case where cancer cells do not die despite the fact. Anticancer drug resistance may vary from patient to patient and may even be triggered by a variety of factors, including genetic differences between tumors derived from the same tissue. Each cancer cell derived from a patient can acquire different genetic characteristics, and the ‘mutation phenomenon’ shows not only the diversity of gene expression, but also the activation of tumor inducers and inactivation of tumor suppressors. As a result, all cancers express anticancer drug resistance genes in different patterns, and cells in one cancer mass acquire diversity for drug resistance. In addition, even if tumors are not basically resistant to specific anticancer treatments, once exposed to anticancer drugs, resistant cells selectively grow based on this diversity, and eventually many cancer cells quickly become resistant to anticancer drugs. In this case, the use of multiple drugs having different target substances in the cell can effectively treat and increase the cure rate. However, in many cases, cells are both structurally and functionally resistant to completely different drugs simultaneously. This phenomenon is called multi-drug resistance (MDR) and is caused by limiting the accumulation of intracellular drugs through limited absorption, increased release of anticancer drugs, or changes in membrane lipids such as ceramides. Multiple drug resistance inhibits apoptosis induced by most anticancer drugs, causes repair of DNA damage and non-toxicity of drugs, and alters the cell cycle, thereby imparting additional resistance to anticancer drugs to cells. .

Therefore, in order to overcome such multiple drug resistance, there is a need to develop a sensitizer capable of reducing resistance to anticancer drugs. Anticancer sensitizers play a role in helping to overcome drug resistance of existing anticancer drugs by increasing the sensitivity of cancer cells to anticancer chemotherapy and kill cancer cells more efficiently. In other words, it is administered together with existing anticancer drugs to overcome resistance and to react sensitively to anticancer treatment again. Previous studies related to sensitizers showed that breast cancer cell lines resistant to doxorubicin showed resistance to resveratrol, and to overcome this, when resveratrol was administered together, it was confirmed that the growth of cancer cells was inhibited, and resveratrol was used as a sensitizer. (Kim, Tae Hyung, et al. "Resveratrol enhances chemosensitivity of doxorubicin in multidrug-resistant human breast cancer cells via increased cellular influx of doxorubicin." Biochimica et Biophysica Acta (BBA)-General Subjects 1840. 1 (2014): 615-625.), when Gefitinib, Erlotinib, and Trastuzumab were administered together in non-small cell carcinoma cells resistant to foretinib, it was confirmed that the growth of cancer cells was inhibited, and the above three anticancer drugs acted as sensitizers. It has been confirmed that (Liu, Li, et al. "Novel mechanism of lapatinib resistance in HER-2 positive breast tumor cells: activation of AXL." Cancer research 69. 17 (2009): 6871-6878.).

It is important to analyze the resistance mechanism of anticancer drugs in order to find a sensitizer that has an optimal effect to overcome the resistance of existing anticancer drugs. However, the mechanisms of resistance to cancer drugs are diverse and can be observed in combination. In other words, since patients suffering from the same disease have different mechanisms of resistance to the same anticancer drug, a sensitizer based on the mechanism of resistance should be proposed in a personalized manner. The wet-experiment method, which has been conducted so far, through in vitro and in vivo experiments, provides accurate and reliable results in finding a sensitizer capable of overcoming the resistance of anticancer drugs. However, it is difficult to analyze all the various mechanisms of resistance of individual cancer patients, and sensitizers that can be tested in terms of labor, time, and cost when selecting the most potent drugs through screening are very limited, so sensitizers that exhibit optimal effects. There is a limit to which it is difficult to find (sensitizer).

Therefore, the present inventors use an in silico (computer model) method instead of the existing in vivo and in vitro methods, extracting the patient's potential resistance genes and resistance mechanisms based on large-scale molecular networks and patient transcript data, and extracting resistance. A method of predicting a sensitizer with an optimal anticancer effect that can effectively act on the mechanism and return to the state before resistance occurred was completed.

It is an object of the present invention to provide a means for efficiently predicting a sensitizer for overcoming resistance of an anticancer agent using a computational method.

To achieve the above object,

The present invention comprises the steps of extracting differentially expressed genes (DEG) by comparing before and after cancer resistance develops (step 1);

Extracting (activity change genes, ACG) genes whose activity change genes (ACGs) change according to the expression change of the DEG (step 2);

Combining the DEG and ACG to extract potential resistance genes (PRG) (step 3);

Extracting resistance mechanism genes (RMG) from the potential resistance genes (PRG) (step 4);

Quantifying the effect of a candidate sensitizer on the resistance mechanism genes (RMG) (step 5); And

Calculating a potential sensitizer score (PSS) of the candidate sensitizer (step 6); It provides a computational method for predicting a sensitizer for overcoming the resistance of an anticancer agent, including.

In addition, the present invention provides a computer-readable medium in which a program including instructions for executing a computational method for predicting a sensitizer for overcoming resistance of an anticancer agent is stored.

In addition, the present invention

A module for extracting differentially expressed genes (DEG) by comparing before and after cancer resistance develops;

A module for extracting genes (activity change genes, ACG) whose protein activity changes according to changes in the expression of the DEG;

A module for extracting potential resistance genes (PRG) by combining the DEG and ACG;

A module for extracting resistance mechanism genes (RMG) among the potential resistance genes (PRG);

A numerical analysis module that quantifies the effect of a candidate drug on the resistance mechanism genes (RMG); And

A module for calculating a potential sensitizer score (PSS) of the candidate drug; It provides a system for predicting a sensitizer for overcoming the resistance of an anticancer agent, including.

When using the present invention, it is possible to select a sensitizing agent having an optimal anti-cancer effect to overcome the resistance of the anti-cancer agent and make it sensitive again. A sensitizer having an optimal anti-cancer effect identified through the present invention may be usefully used for customized treatment according to the advent of the precision medicine era through experimental verification.

1 is a diagram showing the extraction of differentially expressed genes (DEG).
FIG. 2 shows differentially expressed genes (DEG) and genes whose protein activity changes (activity change genes, ACG) on a molecular network, and selects the group containing the largest number of PRGs. It is a diagram extracted as a major resistance mechanism.
3A is a diagram showing the effect of a candidate drug on resistance mechanism genes (RMG) using the RWR algorithm.
Figure 3b is a diagram showing the RWR probability score value for the resistance mechanism gene indicated in yellow.
FIG. 4 is a diagram illustrating a potential sensitizer score (PSS) by adding the RWR probability score of each resistance mechanism genes (RMG) to candidate drugs.
5 is a diagram showing a candidate drug used for prediction of a sensitizer.
6 is a diagram showing AUROC values of a model (SOCAR) of the present invention and a known model (KsRepo).
7 is a diagram showing the Anatomical Therapeutic Chemical Classification (ATC) codes of the top 20 candidate sensitizers.
FIG. 8 is a diagram illustrating GO term enrichment analysis of all resistance mechanism genes (RMG), and analysis of resistance mechanism genes that have a large influence on the top five candidate sensitizers.
9 is a diagram showing the sensitization mechanism of selixipag and axitinib.
10 is a diagram showing cell viability of 12 candidate sensitizers for each experimental group.
control (control): MCF/TAMR cell line without treatment
Tamoxifen 1㎛: MCF/TAMR cell line treated with tamoxifen 1㎛
Sensitizer: MCF/TAMR cell line treated with 5 μm of each candidate sensitizer
Tamoxifen 1㎛ + Sensitizer: MCF/TAMR cell line treated with tamoxifen 1㎛ and each candidate sensitizer 5㎛

Hereinafter, the present invention will be described in detail.

The present invention comprises the steps of extracting differentially expressed genes (DEG) by comparing before and after cancer resistance develops (step 1);

Extracting (activity change genes, ACG) genes whose activity change genes (ACGs) change according to the expression change of the DEG (step 2);

Combining the DEG and ACG to extract potential resistance genes (PRG) (step 3);

Extracting resistance mechanism genes (RMG) from the potential resistance genes (PRG) (step 4);

Quantifying the effect of a candidate sensitizer on the resistance mechanism genes (RMG) (step 5); And

Calculating a potential sensitizer score (PSS) of the candidate sensitizer (step 6); It provides a computational method for predicting a sensitizer for overcoming the resistance of an anticancer agent, including.

The differentially expressed genes (DEG) in step 1 may be differentially expressed genes (DEG) in which mRNA expression is changed by comparing transcript data of cancer cells sensitive to an anticancer drug and anticancer drug resistant cells. .

The cancer of step 1 is breast cancer, bladder cancer, cervical cancer, bile duct cell cancer, chronic myelogenous leukemia (CML), colon cancer, esophageal cancer, stomach cancer, liver cancer, non-small cell lung cancer (NSCLC). ), lymphoma, osteosarcoma, ovarian cancer, pancreatic cancer, prostate cancer, kidney cancer, and small-cell lung cancer (Small-Cell Lung Carcinoma, SCLC), but is not limited thereto.

The gene (ACG) whose protein activity in step 2 is changed may be extracted from a molecular network. A network is composed of a series of points (nodes) and lines (edges) connecting these points, and the molecular network refers to a network that includes biological events, that is, interactions between biological elements that can occur in a biological system. On the molecular network, nucleic acids, proteins, small molecules, chemicals, or metabolites constitute a node, and an edge is a directional line connecting the components. Represents interaction.

The node may be a nucleic acid including a gene, a protein, a small molecule, a chemical, and a metabolite, but is not limited thereto. The molecular network may be a physically directed interaction network between in vivo elements, and specifically, may be a signaling network or an expression control network, but is not limited thereto. Does not.

The molecular network may be extracted from a database providing information on a physically induced interaction between a gene and a protein, or may be directly input through an input means. The input means may be any one selected from the group consisting of a keyboard, a scanner, a touch pad, a barcode input device, a mouse, a tablet, a trackball, an electronic pen, and a digital camera, and is not limited thereto as long as data can be input. The database is preferably KEGG pathway, SIGNOR or TRANSFAC, but is not limited thereto.

The activity change genes (ACG) of the protein in step 2 are genes whose activity of the encoding protein changes due to differentially expressed genes (DEG) of the upstream protein. In other words, the mRNA expression change of the gene whose protein activity changes (ACG) itself is not significant, so it is not extracted as a gene whose expression changes (DEG), but the change in mRNA expression of the gene whose upstream expression changes (DEG) is a downstream gene. It is based on the assumption that it may affect the protein activity of and the change in the protein activity may affect anticancer drug resistance.

The resistance mechanism genes (RMG) of step 4 are genes that exist in the major resistance mechanism pathway. The main resistance mechanism is the group containing the largest number of PRGs after mapping the potential resistance genes (PRG) of step 3 into the molecular network and excluding uncombined components. It can be determined by extracting the (largest connected component). That is, the potential resistance gene (PRG) of step 3 forms several independent groups on the molecular network, and the major resistance mechanism is extracted by selecting a group containing the largest number of PRGs, The genes in the major resistance mechanism pathways are determined as resistance mechanism genes (RMG). This is based on the assumption that proteins related to the same disease interact with each other and tend to form clusters on a network, and that they will show the same tendency not only in disease but also in resistance mechanisms.

The quantification of the effect of the candidate sensitizer on the resistance mechanism gene of step 5 can be calculated using the RWR algorithm of Equation 1 below. The Random Walk with Restart (RWR) algorithm is a random walk-based ranking technique, and the random walk refers to moving to a neighboring node by selecting a neighboring edge randomly from a starting node on a graph. If the random walk is repeatedly moved infinitely, the probability of visiting each node comes out. There is a random walker at the starting node, and the random walker can move to another connected node with a certain probability. The random worker of the present invention starts from a drug target and repeatedly moves from a current node to an adjacent node. In this process, the movement is repeated with a restart probability (r) for each movement. Therefore, by applying the RWR algorithm, it is possible to calculate the RWR probability score of each resistance mechanism gene.

[Equation 1]

: 분자 네트워크에 대한 정규화 된 인접 매트릭스,

: 재시작(restart) 확률,

: 시작 벡터,

: 확률 벡터,

:RWR 시뮬레이션의 결과)(

: Normalized adjacency matrix over molecular network,

: Probability of restart,

: Start vector,

: Probability vector,

:RWR simulation result)

The potential sensitizer score of step 6 can be calculated by Equation 2 below.

: 후보약물,

: 각 유전자의 RWR 확률 점수(RWR probability score),

: 내성 약물,

: 내성 기전 유전자(resistance mechanism gene)들의 집합)(

: Candidate drugs,

: RWR probability score of each gene,

: Resistant drugs,

: A collection of resistance mechanism genes)

The RWR probability score of each resistance mechanism gene derived by the method of step 5 is calculated, and the value of the sum of the RWR probability scores of the top five genes with the highest RWR probability score excluding the drug target as the starting node is the value of the potential sensitizer. (potential sensitizer score, PSS). Candidate sensitizers with high levels of potential sensitizers may be potent sensitizers capable of overcoming anticancer drug resistance.

After step 6, the step of generating visualization data for the information derived through the visualization means and outputting the same as visualization data through the output means may be additionally included. The output means may be any one selected from the group consisting of a monitor, a printer, and a plotter, and any means capable of outputting a result may be used.

In addition, the present invention provides a computer-readable medium in which a program including instructions for executing a computational method for predicting a sensitizer for overcoming resistance of the anticancer agent is stored. The computer-readable recording medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tapes, floppy disks, optical data storage devices, etc., and also implemented in the form of carrier waves (for example, transmission through the Internet). Include. In addition, the computer-readable recording medium can be distributed over a network-connected computer system to store and execute computer-readable codes in a distributed manner.

In addition, the present invention is a module for extracting differentially expressed genes (DEG) by comparing before and after resistance to cancer occurs;

A module for extracting genes (activity change genes, ACG) whose protein activity changes according to changes in the expression of the DEG;

A module for extracting potential resistance genes (PRG) by combining the DEG and ACG;

A module for extracting resistance mechanism genes (RMG) having a high correlation among the potential resistance genes (PRG);

A numerical analysis module that quantifies the effect of a candidate drug on the resistance mechanism genes (RMG); And

A module for calculating a potential sensitizer score (PSS) of the candidate drug; It provides a system for predicting a sensitizer for overcoming the resistance of an anticancer agent, including.

The system may additionally include an input module for inputting a molecular network through a computer input means, and a module for generating visualization data for the information derived by the modules and an output module for outputting the same as visualization data. May contain additionally.

The input module may be any one selected from the group consisting of a keyboard, a scanner, a touch pad, a barcode input device, a mouse, a tablet, a trackball, an electronic pen, and a digital camera, and is not limited thereto as long as data can be input.

The output module may be any one selected from the group consisting of a monitor, a printer, and a plotter, and any means capable of outputting a result may be used.

In a specific embodiment of the present invention, a gene with a change in expression (DEG) is extracted by comparing patient transcript data, and a gene with a change in protein activity (ACG) is extracted by constructing a large-scale molecular network. (PRG) was selected. The group (largest connected component) containing the largest number of potential resistance genes (PRG) in the molecular network is extracted and selected as resistance mechanism genes (RMG), and the effect of candidate sensitizers on the resistance mechanism genes. The effect was quantified and the mechanism of the sensitizer with the highest level was selected and analyzed. The sensitizer prediction model of the present invention has the advantage of superior performance than a known similar model. Accordingly, the method of the present invention can be effectively used for predicting and verifying a sensitizer for overcoming anticancer drug resistance for personalized drug prescription in the era of precision medicine.

Hereinafter, the present invention will be described in detail by examples.

However, the following examples are merely illustrative of the present invention, and the contents of the present invention are not limited by the following examples.

< Example 1> To overcome anticancer drug resistance Sensitizer (sensitizer) design of predictive model

Through a case study, ER-positive breast cancer, which accounts for 80% of all breast cancers, was selected as the target disease. Tamoxifen, the most widely used anticancer agent for the treatment of ER-positive breast cancer, has excellent anticancer effects, but is known to induce drug resistance in 40% of ER-positive breast cancer patients due to long-term administration. In the present invention, a sensitizer for overcoming tamoxifen resistance in ER-positive breast cancer was predicted by the method of the following examples.

<1-1> Extraction of differentially expressed genes (DEG)

Genes that change mRNA expression by comparing the transcript data of 8 types of tamoxifen-sensitive MCF7 breast cancer cell line and 3 types of tamoxifen-resistant MCF7 breast cancer cell line (differentially expressed genes, DEG) were extracted. The transcript data was collected in the Gene Expression Omnibus database (NCBI acc. number: GSE67916). Genes with an FDR value of less than 0.05 using the R language LIMMA package that can analyze changes in gene expression and a False Discovery Rate (FDR) threshold as a cutoff criterion to select DEGs whose expression is significantly changed. Was extracted.

As a result, 801 differentially expressed genes (DEGs) were extracted from tamoxifen-resistant breast cancer cells among a total of 11969 genes (FIG. 1).

<1-2> Design of molecular network

Molecular networks were extracted from three public databases (KEGG pathway, SINOR, TRANSFAC). The molecular network was designed by dividing into a signaling network and a regulatory network. The signaling network consists of physically directed interactions between the KEGG pathway and human proteins derived from the SIGNOR database, and the regulatory network is the KEGG pathway, SIGNOR database, and TRANSFAC. It consists of a physically directed interaction between a human protein derived from and a human gene.

The KEGG pathway used to design the signal transduction network and expression control network was used after downloading the KEGG Markup Language (KGML) file corresponding to each pathway and preprocessing the data using the KEGG graph package in R studio. Of the pathways provided by KEGG, the signal transduction network was extracted using seven types (activation, inhibition, phosphorylation, dephosphorylation, glycosylation, ubiquitination, and methylation), and the expression control network was constructed in two types (expression, repression). Extracted.

The SIGNOR database contains interactions between various types of molecules such as proteins, chemicals, and small molecules, and each molecular interaction has its own mechanism. Therefore, the expression control network (regulatory network) was designed by extracting the interactions (transcriptional regulation, transcriptional activation, and transcriptional inhibition) according to the type of mechanism. In addition, a signaling network was designed by extracting the interactions between proteins along with other types of mechanisms such as phosphorylation and ubiquitination. In addition, the expression control network was extracted using TRANSFAC as well.

As a result, a large-scale molecular network was constructed consisting of 42344 signaling interactions and regulatory interactions between 6910 proteins or genes.

<1-3> Extraction of activity change genes (ACG)

Since only extracting the genes whose expression changes (DEGs) of Example 1-1 could not take into account all genes related to resistance, genes that change the activity of proteins (activity change genes (ACG)) were additionally extracted. .

Genes that change the activity of a protein (activity change genes, ACG) mean that the change in mRNA expression of the gene is not significant, but the activity of the protein encoded by the gene (DEG) whose upstream expression changes (DEG) is changed. It is a gene whose activity of downstream proteins changes due to a signal transduction relationship. That is, the gene was extracted under the assumption that there is a possibility that the altered activity of the protein encoded by ACG may cause resistance.

In a signaling network consisting of 27067 signaling interactions between 4620 proteins constructed in Example 1-2, 801 expression-changing genes extracted in Example 1-1 (DEG ) As a result of matching, 211 genes whose expression changes (DEG) were mapped. Genes (ACGs) whose activity of 211 DEG downstream proteins was changed were extracted, and these genes were not extracted in Example 1-1.

As a result, genes that change the activity of 670 proteins (activity change genes (ACG)) were extracted, and the AKT, PI3K, EGFR and CTNNB1 genes were not extracted from the genes whose expression changes (DEG), but the activity of the protein was changed. It was extracted with gene (ACG). Tamoxifen is known to bind to 18 targets, of which one is DEG and 9 is ACG, which overlaps the target gene of tamoxifen. Therefore, the above results suggest the possibility that dysregulation occurring in the mechanism of action of tamoxifen is related to drug resistance (Fig. 2).

<1-4> Extraction of potential resistance genes (PRG)

A total of 801 differentially expressed genes (DEG) extracted in Example 1-1 and activity change genes (ACG) of 670 proteins extracted in Example 1-3 were added. 1471 potential resistance genes (PRG) were extracted (Fig. 2).

<1-5> Extraction of resistance mechanism genes (RMG)

Based on research findings that disease-related genes and proteins interact with each other and tend to form network clusters (Barabasi, A. -L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach) to human disease.Nature reviews genetics 12, 56 (2011)), it was assumed that the same tendency would be observed not only in disease but also in resistance mechanisms.

Specifically, only the potential resistance genes (PRG) of Example 1-4 were extracted from the molecular network constructed in Example 1-2. The extracted PRGs are connected to each other on a molecular network and exist as several independent groups. At this time, the PRGs in each group were connected to each other, and the group (largest connected component) containing the largest number of PRGs was selected and extracted, and this was regarded as a major resistance mechanism. The major resistance mechanism consists of 877 PRGs, that is, 220 DEGs and 657 ACGs, and 877 PRGs were named resistance mechanism genes (RMGs) (FIG. 3A).

In addition, GO term enrichment analysis (Eden, E., Navon, R., Steinfeld, I., Lipson, D., & Yakhini, Z.) of the resistance mechanism genes to extract the biological process related to tamoxifen resistance (2009).GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists.BMC Bioinformatics, 10(1), 48.) was performed. Among the 4354 biological mechanisms, 1541 mechanisms were found to have a high relationship in the resistance mechanism gene (RMG), and the top 20 mechanisms were selected (Table 1).

ranking Gene ontology term False Discovery Rate (FDR) One neurotrophin signaling pathway (GO:0038179) 1.37E-87 2 neurotrophin TRK receptor signaling pathway (GO:0048011) 3.07E-86 3 positive regulation of kinase activity (GO:0033674) 7.51E-74 4 response to peptide (GO:1901652) 7.51E-74 5 cellular response to nitrogen compound (GO:1901699) 2.24E-70 6 immune response-regulating cell surface receptor signaling pathway (GO:0002768) 1.09E-69 7 response to peptide hormone (GO:0043434) 1.09E-69 8 cellular response to organonitrogen compound (GO:0071417) 4.14E-68 9 cellular response to hormone stimulus (GO:0032870) 1.87E-66 10 fibroblast growth factor receptor signaling pathway (GO:0008543) 1.25E-64 11 response to fibroblast growth factor (GO:0071774) 1.13E-63 12 epidermal growth factor receptor signaling pathway (GO:0007173) 1.52E-63 13 positive regulation of protein kinase activity (GO:0045860) 1.92E-63 14 cellular response to fibroblast growth factor stimulus (GO:0044344) 1.92E-63 15 ERBB signaling pathway (GO:0038127) 6.35E-63 16 blood coagulation (GO:0007596) 1.46E-62 17 coagulation (GO:0050817) 1.46E-62 18 hemostasis (GO:0007599) 7.07E-62 19 cellular response to peptide (GO:1901653) 7.55E-62 20 cellular response to peptide hormone stimulus (GO:0071375) 1.38E-60

<1-6> Selection of candidate sensitizers

To predict candidate sensitizers, it was hypothesized that if a candidate drug had a great influence on the regulation of the resistance mechanism gene (RMG), it could be restored to the state before resistance occurred and there was a possibility of increasing the sensitivity of the anticancer drug to cancer cells.

Under this assumption, the effect of candidate sensitizers on 877 resistance mechanism genes (RMGs) in the molecular network was simulated using the Random Walk with Restart (RWR) algorithm of Equation 1 below (FIG. 3A).

[Equation 1]

: 분자 네트워크에 대한 정규화 된 인접 매트릭스,

: 재시작(restart) 확률,

: 시작 벡터,

: 확률 벡터,

:RWR 시뮬레이션의 결과)(

: Normalized adjacency matrix over molecular network,

: Probability of restart,

: Start vector,

: Probability vector,

:RWR simulation result)

In Equation 1, the W value is a normalized adjacency matrix for a molecular network composed of signal transduction interactions and expression control interactions, and r, which is a restart probability, was set to 0.7. The starting vector P0 is set to 30 for the starting node and 0 for the other nodes. The probability vector Pt was repeatedly updated until the difference between Pt+1 and Pt became less than 1E-05. Pt+1 is the result of RWR simulation.

Since the random walker starts at a drug target and moves to an adjacent gene, drugs whose targets are not included in the molecular network were excluded from DrugBank's drug list. 4009 drugs were simulated by the RWR algorithm, among which 13 drugs known as sensitizers and 3996 unknown drugs were included (Fig. 5). After performing the RWR algorithm, all genes have RWR probability scores, and RWR probability scores of genes for resistance mechanisms were selected (FIG. 3B). By summing the RWR probability scores of the selected resistance mechanism genes, a potential sensitizer value (PSS) of 0-30 was calculated by Equation 2 below (FIG. 1D). In addition, DCDB and Twosides database were used to confirm whether candidate sensitizers were effective and safe for coadministration with tamoxifen. The two databases provide information on side effects and drugs having a synergetic/antagonistic effect when a drug is administered concurrently.

[Equation 2]

: 후보약물,

: RWR 확률 점수(RWR probability score),

: 내성 약물,

: 내성 기전 유전자(resistance mechanism gene)들의 집합)(

: Candidate drugs,

: RWR probability score,

: Resistant drugs,

: A collection of resistance mechanism genes)

As a result, the average value of the potential sensitizer value (PSS) of all 4009 genes was 7.97, and the average value of 1358 FDA-approved drugs was 7.72, and 20 candidate sensitizers with a high potential sensitizer level (PSS) were selected. (Fig. 4 and Table 2). As a result of analyzing the Anatomical Therapeutic Chemical Classification (ATC) codes of 20 selected candidate sensitizers from DrugBank, it was confirmed that they belong to 9 classifications out of 14 first level ATC codes (FIG. 7). As a result of analysis using DCDB and Twosides database, no side effects or synergetic/antagonistic effects were known when co-administered with tamoxifen among 20 candidate sensitizers. In the above results, gefitinib ranked 27th and metformin ranked 31st are known to have the potential to cause side effects such as renal tubular acidosis or carotid bruit in tamoxifen-resistant cells. They have the potential to become candidate sensitizers without side effects when administered with existing anticancer drugs.

ranking Drug name DrugBank ID One Selexipag DB11362 2 Axitinib DB06626 3 Framycetin DB00452 4 Plerixafor DB06809 5 Sunitinib DB01268 6 Lenvatinib DB09078 7 Pegademase Bovine DB00061 8 Ramucirumab DB05578 9 Cinacalcet DB01012 10 Tirofiban DB00775 11 Insulin Lispro DB00046 12 Insulin Glargine DB00047 13 Anakinra DB00026 14 Eptifibatide DB00063 15 Ado-Trastuzumab Emtansine DB05773 16 Pertuzumab DB06366 17 Nintedanib DB09079 18 Chloramphenicol DB00446 19 Afatinib DB08916 20 Insulin Aspart DB01306

< Example 2> Sensitizer Predictive model ( SOCAR ) And comparison with existing models

<2-1> Performance evaluation of sensitizer prediction model

Area under the receiver operating characteristic curve (AUROC) values were compared to evaluate the performance of the sensitizer prediction model of Example 1. The model of the present invention showed an AUROC value of 0.87 for 1358 FDA approved drugs, and an AUROC value of 0.75 for all 4009 drugs. In the present invention, since the activity change genes (ACG) were first defined, in order to measure the effect of ACG on the predictive performance of a sensitizer, a resistance mechanism gene considering only the genes whose expression changes (DEG) The AUROC values were compared in two cases of (RMG) and resistance mechanism gene (RMG), which considered DEG and ACG together.

As a result, the AUROC value of the RMG model considering only DEG showed a value of 0.78 for FDA approved drugs, and the AUROC value of the RMG model considering both DEG and ACG showed a value of 0.87 for FDA approved drugs. It was confirmed that the performance of the RMG model considering both DEG and ACG was excellent (FIG. 6).

<2-2> Comparison with existing model

In silico analysis models predicting sensitizers to overcome anticancer drug resistance have not been reported so far. Therefore, the performance was compared with KsRepo, the most comparable model.

Specifically, KsRepo is a drug repositioning model that outputs candidate drugs as scores when a disease-related gene is input. KsRepo is similar in purpose to the model of the present invention from the viewpoint of predicting effective drugs based on individual disease data and predicting drug effects in a personalized manner. The gene whose expression of Example 1-1 is changed (DEG) was input, and the drug output as a candidate drug was determined as a candidate sensitizer.

As a result, KsRepo's AUROC value for FDA-approved drugs was 0.61, and AUROC values for all drugs were 0.63. The above results suggest that the SOCAR model of the present invention shows superior performance than the existing KsRepo model (FIG. 6).

< Example 3> Candidate Of the sensitizer Mechanism analysis

<3-1> Mechanism analysis of candidate sensitizers

Among the drugs proposed as candidate sensitizers, in-depth analysis was performed on FDA-approved drugs. As a result, according to the ATC code available from DrugBank, 9 of the top 20 drugs were anticancer drugs, 2 of them were used for breast cancer, and the remaining 7 were used for other types of cancer. The other 11 drugs were used to treat non-cancer diseases such as hypertension and diabetes mellitus.

To analyze the mechanism by which the sensitizer works, the mechanisms of the top five drugs (selexipag, axitinib, framycetin, plerixafor, sunitinib) among the 20 sensitizer candidates in Table 2 were analyzed by GO term enrichment analysis (Eden, E., Navon, R). ., Steinfeld, I., Lipson, D., & Yakhini, Z. (2009).GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists.BMC Bioinformatics, 10(1), 48.) Analyzed. For each drug, the top 20 GO term biological processes were extracted from the top 50 resistance mechanism genes (RMG) with a high RWR probability score (FDR>0.001) (FIG. 8). As a result, the top 20 GO term mechanisms of each candidate drug substantially overlapped with the 877 resistance mechanism genes (RMG) extracted in Example 1-4. The above results show that the top five candidate sensitizer drugs have the potential to act on a major mechanism of resistance and reverse it before resistance develops. The top five drugs were found to have a great effect on the top two mechanisms in Table 1, the neutrophin TRK receptor signaling pathway (GO:0048011) and the neutrophin signaling pathway (GO: 0038179). Since targeting neutrophin results in inhibition of tumor cell growth and metastasis of breast cancer, the top five candidate drugs restore signal transduction related to neutrophin altered by resistance, making tamoxifen sensitive to cancer cells again. It suggests that there is a possibility to do it.

<3-2> Mechanism analysis of selexipag

In Table 2, a sensitization mechanism was analyzed for selexipag, a candidate sensitizer having the highest potential sensitizer level (PSS).

Specifically, selexipag is a cellular response to glucagon stimulus in addition to the neutrophin TRK receptor signaling pathway (GO: 0048011) and the neutrophin signaling pathway (GO: 0038179), which are the mechanisms that the top five candidate sensitizers have the most influence in Example 3-1. (GO:0071377) and response to glucagon (GO:0033762) have a great influence. Glucagon, as a peptide hormone, activates GCCR and induces the activities of GNAS, ADCY, cAMP, and PKA proteins at the bottom. Among them, the PKA protein plays a role in inhibiting cell growth by activating the extrinsic apoptosis pathway induced outside of MCF7 cells. In tamoxifen resistance conditions, genes encoding GNAS, ADCY, and PKA proteins were extracted as genes whose protein activity changes (ACG). In tamoxifen-resistant MCF7 cells, the expression of two proteins (RAMP1, IGF1R) among the three upstream proteins (RAMP1, IGF1R, and LPAR6) that activates GNAS was reduced, so it is presumed that the expression of GNAS was reduced, and the intensity of the GNAS activity signal. Is weakened, reducing PKA activity, resulting in increased cell growth. selexipag is an agonist of PTGIR, which increases GNAS activity and inhibits cell growth, thereby reversing it before tamoxifen resistance occurs (FIG. 9).

<3-3> Mechanism analysis of axitinib

In Table 2, a sensitization mechanism was analyzed for axitinib, a candidate sensitizer having the second highest sensitizing agent level.

Specifically, axitinib is not only the neutrophin TRK receptor signaling pathway (GO: 0048011) and the neutrophin signaling pathway (GO: 0038179), which are the mechanisms that the top five candidate sensitizers in Example 3-1 have the most influence, the EGFR signaling pathway (GO :0007173) and the ERBB signaling pathway (GO:0038127). EGFR (HER1) and ERBB (HER2) promote cell growth, proliferation, angiogenesis and cell survival through mediating proteins such as MAPK and BAD (Fig. 9). Proteins downstream of HER1 and HER2 under tamoxifen resistance conditions were extracted as genes (ACGs) whose protein activity changes. The expression of four genes (PTPN21, PIK3R1, ITGA6, FGFR2) of the upstream genes (IGF1R, PTPN21, PIK3R1, ITGA6, FGFR2) of SRC, which is an upstream modulator farthest from the mediating proteins, was increased, PI3K has the expression of seven of the ten upstream genes (PGR, ERBB4, IGF1R, IRS2, LYN, LIFR, FGFR2, NRAS, FYN, GAB1) (IRS2, LYN, LIFR, FGFR2, NRAS, FYN, GAB1). As it was increased, it is assumed that the expression of SRC and PI3K would have been increased. MAPK and BAD activation induced by increased expression of SRC and PI3K increases cell growth, proliferation, angiogenesis and cell survival, and leads to tamoxifen resistance. Axitinib acts as an antagonist of VEGFR and reduces the activity of upper-level modulators such as SRC and PI3K, thereby reverting abnormal cellular behavior to before resistance develops (FIG. 9).

< Experimental example 1> Of the sensitizer Verification of anticancer drug resistance overcoming effect

The following experiment was performed to verify the effect of overcoming anticancer drug resistance of drugs derived from candidate sensitizers using the sensitizer prediction method of Example 1.

Specifically, tamoxifen-resistant MCF7 breast cancer cell line (hereinafter, MCF/TAMR) (AX4011, AXOL Bioscience, Cambridge, UK) was used in 10% fetal bovine serum (FBS, Cat # 16000044, Gibco, California, USA) and 6 ng/ml insulin. (Cat # 41400045, Gibco, California, USA) was added and cultured in DMEM/F-12 GlutaMAX supplemented medium (Cat # 10565018, Gibco, California, USA) that did not contain phenol red. The cultured MCF/TAMR cell line was dispensed into a 96-well plate at 4×10 ³ cells/well, and treated with 1 μm tamoxifen and/or 12 sensitizers 5 μm. 48 hours after treatment, cell viability was measured through WST-8 analysis (CCK-8, Dojindo, Kumamoto, Japan). After incubation at 37° C. for 4 hours by adding WST-8 reagent, absorbance was measured at a wavelength of 450 nm using an xMark Microplate Absorbance Spectrophotometer (Bio-Rad, California, USA). The survival rate of cells treated with tamoxifen and/or sensitizer to control cells that were not treated with anything was derived by the following calculation formula 1. In order to increase the accuracy, three or more independent experiments were performed.

<Calculation 1>

Cell viability (%) = (absorbance of wells treated with tamoxifen and/or sensitizer-absorbance of blank wells)/(absorbance of control wells-absorbance of blank wells)

In order to analyze the difference in the viability of various cells for each experimental group derived by Equation 1, a student t-test with an assumption of equal variance was performed using R version 3.2.0 (R Foundation for Statistical Computing, Austria). .

As a result, in MCF/TAMR cells resistant to tamoxifen, cell viability (98.22±1.102%) hardly decreased when only tamoxifen was treated. In addition, when only six drugs, selexipag, plerixafor, sunitinib, lenvatinib, eptifibatide and chloramphenicol, were treated with a single treatment, cell viability was more than 90%, showing multi-drug resistance to drugs other than tamoxifen. All of the candidate sensitizers used in the experiment were observed to show the effect of the combined administration due to decreased cell viability compared to the group treated with tamoxifen only and the group treated with a single sensitizer, 10 compared to the control group without treatment. Types of candidate sensitizers have been shown to significantly inhibit cell growth (p<0.0005). In addition, the combined use of pertuzumab and tamoxifen showed the most significantly improved anticancer drug resistance overcoming effect compared to the control group (p <0.0001) and the group treated with pertuzumab alone (p=0.0251) (Table 3 and FIG. 10). The above results show that the candidate sensitizer derived by the sensitizer prediction method according to the present invention actually has an effect of overcoming anticancer drug resistance.

Sensitizer name process Tamoxifen throughput Sensitizer throughput cell
Viability (%) ± standard error of measurement (SEM) p-value (relative to control) P-value (compared to the sensitizer only group) Selexipag Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 90.52 1.178 0.056 Tamoxifen + Sensitizer 1 uM 5 uM 83.65 0.8484 0.0002 0.0009 Axitinib Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 86.78 1.196 <0.0001 Tamoxifen + Sensitizer 1 uM 5 uM 79.2 1.378 <0.0001 0.0006 Plerixafor Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 90.44 1.878 0.0062 Tamoxifen + Sensitizer 1 uM 5 uM 78.62 1.144 <0.0001 <0.0001 Sunitinib Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 90.41 0.7089 0.0075 Tamoxifen + Sensitizer 1 uM 5 uM 82.82 1.748 <0.0001 0.0008 Lenvatinib Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 95.78 2.45 0.1779 Tamoxifen + Sensitizer 1 uM 5 uM 86.52 2.368 0.0051 0.0278 Cinacalcet Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 87.23 3.026 0.0007 Tamoxifen + Sensitizer 1 uM 5 uM 74.04 2.828 <0.0001 0.0052 Tirofiban Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 89.52 1.602 0.0118 Tamoxifen + Sensitizer 1 uM 5 uM 77 3.084 <0.0001 0.0136 Eptifibatide Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 93.54 2.343 0.2536 Tamoxifen + Sensitizer 1 uM 5 uM 82.07 3.349 0.0009 0.0125 Pertuzumab Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 1 uM 88.51 2.296 0.0234 Tamoxifen + Sensitizer 1 uM 1 uM 77.64 2.858 <0.0001 0.0251 Nintedanib Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 70.28 1.32 <0.0001 Tamoxifen + Sensitizer 1 uM 5 uM 62.4 1.967 <0.0001 0.0007 Chloramphenicol Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 91.09 2.664 0.0338 Tamoxifen + Sensitizer 1 uM 5 uM 81.45 3.356 <0.0001 0.021 Afatinib Control - - 100 1.549 - Tamoxifen 1 uM - 98.22 1.102 0.6065 Sensitizer - 5 uM 88.19 2.633 0.0057 Tamoxifen + Sensitizer 1 uM 5 uM 77.65 1.66 <0.0001 0.0105

Claims (8)

Comparing differentially expressed genes (DEG) before and after cancer resistance develops (step 1);
Extracting (activity change genes, ACG) genes whose activity change genes (ACGs) change according to the expression change of the DEG (step 2);
Combining the DEG and ACG to extract potential resistance genes (PRG) (step 3);
Extracting resistance mechanism genes (RMG) from the potential resistance genes (PRG) (step 4);
Quantifying the effect of a candidate sensitizer on the resistance mechanism genes (RMG) (step 5); And
Calculating a potential sensitizer score (PSS) of the candidate sensitizer (step 6); Computational method for predicting a sensitizer for overcoming resistance of an anticancer agent comprising a.
The method of claim 1, wherein the cancer is breast cancer, bladder cancer, cervical cancer, bile duct cell cancer, chronic myelogenous leukemia (CML), colon cancer, esophageal cancer, stomach cancer, liver cancer, non-small cell lung cancer (Non-Small-Cell Lung). Carcinoma, NSCLC), lymphoma, osteosarcoma, ovarian cancer, pancreatic cancer, prostate cancer, kidney cancer, and small-cell lung cancer (Small-Cell Lung Carcinoma, SCLC). sensitizer).
The computational method of claim 1, wherein the gene (ACG) whose activity of the protein in step 2 is changed is extracted from a molecular network, for predicting a sensitizer for overcoming resistance of an anticancer agent. .
The method according to claim 1, wherein the activity change genes (ACG) of the protein in step 2 are differentially expressed genes (DEG) that change the activity of the protein. A computational method for predicting a sensitizer for overcoming the resistance of an anticancer agent.
The method of claim 1, wherein the quantification of the effect of the candidate drug on the resistance mechanism genes (RMG) of step 5 is simulated through a random walk with restart (RWR) algorithm. Computational method for predicting sensitizers.
The method of claim 1, wherein the potential sensitizer score (PSS) of step 6 is calculated by an equation represented by Equation 1 below. A sensitizer for overcoming resistance of an anticancer agent is used. Computational method of prediction.

(

: Candidate drugs,

: RWR probability score,

: Resistant drugs,

: A collection of resistance mechanism genes)
A computer-readable medium in which a program containing instructions for executing a computational method for predicting a sensitizer for overcoming resistance of an anticancer agent according to any one of claims 1 to 6 is stored.
A module for extracting differentially expressed genes (DEG) by comparing before and after cancer resistance develops;
A module for extracting genes (activity change genes, ACG) whose protein activity changes according to changes in the expression of the DEG;
A module for extracting potential resistance genes (PRG) by combining the DEG and ACG;
A module for extracting resistance mechanism genes (RMG) having a high correlation among the potential resistance genes (PRG);
A numerical analysis module that quantifies the effect of a candidate drug on the resistance mechanism genes (RMG); And
A module for calculating a potential sensitizer score (PSS) of the candidate drug; A system for predicting a sensitizer for overcoming the resistance of an anticancer agent comprising a.

Images