Apparatus for determining optimal combination of organoid culture media, method for determining optimal combination by calculating gene expression levels for culture medium combinations, and computer program

Abstract

According to any one of the aforementioned means to solve the problem, with respect to combinations of a plurality of culture medium conditions inputted by a user, the optimal combination of culture medium conditions for an organoid can be determined by using a result distribution diagram of outcome values, such as a differentiation success rate, reproducibility, and gene expression levels of cells cultured with the combinations of the culture medium conditions.

Description

Device for determining optimal organoid culture medium combinations, method for determining optimal combinations by calculating gene expression levels for culture medium combinations, and computer program

[0001] An embodiment of the present disclosure relates to a device for determining an optimal organoid culture medium combination, a method for calculating gene expression levels for culture medium combinations to determine an optimal combination, and a computer program, wherein, for a plurality of combinations of culture medium conditions input by a user, the device compares a plurality of combinations of culture medium conditions using gene expression levels in tumor cells cultured with the combinations of culture medium conditions to determine an optimal combination of culture medium conditions.

[0002] Cancer is one of the most common causes of death worldwide. Approximately 10 million new cases occur each year, accounting for approximately 12% of all deaths, making it the third leading cause of death.

[0003] To develop treatments for cancer, analysis and research on organoids are necessary, so sample organoids are cultured and utilized for research and analysis.

[0004] Cultured organoids can be used to analyze the genetic or molecular characteristics of tumors. Furthermore, cultured organoids can be utilized for new drug testing, studying tumor growth and metastasis mechanisms, and screening assays for drug discovery and testing. Furthermore, cultured organoids can be used to create cancer models for in vitro and in vivo experiments, such as patient-derived organoids and xenografts.

[0005] Culture medium conditions are crucial for culturing organoids. The amount of organoids cultured varies significantly depending on the culture medium conditions. Organoid culture medium conditions vary depending on various factors, including organoid type, desired experimental results, and resource availability. Organoids cultured in inappropriate culture medium conditions cannot be used for research and / or analysis. In particular, gene expression levels also vary between organoids cultured in different culture medium conditions.

[0006] In cells, overexpression of genes has the effect of increasing the regenerative capacity of the cells, and suppression of gene expression prevents the growth of malignant cells. In clinical treatment, suppression of expression is also used for treatment.

[0007] Therefore, there is a growing need to provide optimized culture medium conditions that match the expression level of a specific gene with the expression level of a reference gene for the collected organoids.

[0008] The embodiments disclosed herein aim to provide a device and method for determining a combination of optimal culture medium conditions for cells by using a result distribution for result values ​​such as gene expression levels of cells cultured with a combination of multiple culture medium conditions input by a user.

[0009] According to embodiments of the present disclosure, a method may include a step in which a decision device obtains data from a user, including growth factors for a first organoid, a combination of first culture medium conditions including concentration values ​​of the growth factors, a combination of second culture medium conditions, a combination of third culture medium conditions, and a reference gene expression level of a first gene; a step in which the decision device inputs the combinations of the first to third culture medium conditions and information about the first organoid into an optimal condition determination model, compares result distributions for the combinations of the first to third culture medium conditions, and outputs an optimal condition combination based on the gene expression level of the first gene; and a step in which the decision device outputs output data that visually outputs an optimal condition combination in which the first organoid is cultured while being expressed at the reference gene expression level of the first gene.

[0010] The above optimal condition determination model may be a model trained using deep learning or machine learning, using as input the result distributions generated based on the gene expression level values ​​of the first gene in organoids cultured under the plurality of culture medium conditions and combinations of the plurality of culture medium conditions for a plurality of organoids.

[0011] The above optimal condition determination model can generate a result distribution including the gene expression level of the first gene by sampling the experimental results cultured with each combination of culture medium conditions.

[0012] The above optimal condition decision model can generate distribution maps of results sampled using the Thompson sampling method.

[0013] The above optimal condition determination model may be a reinforcement learning model through a process of relearning using result values ​​including the gene expression level value of the first gene obtained by increasing the number of sampling times for culturing the first organoid under the first to third culture medium conditions.

[0014] The above result distribution may be a visual representation of the gene expression levels obtained for organoids cultured under a combination of culture medium conditions.

[0015] According to embodiments of the present disclosure, a device includes a memory, a communication unit, and a processor, wherein the processor obtains data including a combination of first culture medium conditions including growth factors for a first organoid, concentration values ​​of the growth factors, a combination of second culture medium conditions, a combination of third culture medium conditions, and a reference gene expression level of a first gene from a user, inputs the combination of the first to third culture medium conditions and information about the first organoid into an optimal condition determination model, compares result distributions for combinations of the first to third culture medium conditions, and outputs an optimal condition combination based on the gene expression level of the first gene, and outputs output data that visually outputs the optimal condition combination in which the first organoid is cultured while being expressed at the reference gene expression level of the first gene.

[0016] A computer program according to an embodiment of the present invention can be stored in a medium to execute any one of the methods according to an embodiment of the present invention using a computer.

[0017] In addition, other methods for implementing the present invention, other systems, and computer-readable recording media recording a computer program for executing the above methods are further provided.

[0018] Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims and detailed description of the invention.

[0019] According to any one of the aforementioned problem solving means, for a combination of multiple culture medium conditions input by a user, the optimal combination of culture medium conditions for organoids can be determined by using a result distribution for results such as differentiation success rate, reproducibility, and gene expression level of cells cultured with the combination of culture medium conditions.

[0020] Figure 1 is a block diagram of a device for determining the optimal organoid culture medium combination according to embodiments of the present disclosure.

[0021] Figure 2 is a block diagram of the optimal condition determination unit.

[0022] Figure 3 is a block diagram of a learning device that learns an optimal condition decision model.

[0023] Figure 4 is an exemplary diagram of combinations of culture medium conditions for organoids to be differentiated into vascular cells.

[0024] FIG. 5a, FIG. 5b, and FIG. 5c are exemplary drawings of result distributions generated according to embodiments of the present disclosure.

[0025] Figure 6 is a flowchart of an optimal condition output method according to embodiments of the present disclosure.

[0026] Figure 7 is a flowchart of an optimal condition output method according to other embodiments of the present disclosure.

[0027] The configuration and operation of the present invention will be described in detail with reference to embodiments of the present invention illustrated in the attached drawings below.

[0028] The present invention is capable of various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention, as well as the methods for achieving them, will become clearer with reference to the embodiments described in detail below, along with the drawings. However, the present invention is not limited to the embodiments disclosed below and can be implemented in various forms.

[0029] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. When describing with reference to the drawings, identical or corresponding components are given the same drawing reference numerals, and redundant descriptions thereof will be omitted.

[0030] Hereinafter, the term “upper” or “upper” may include not only things that are directly above in contact, but also things that are above in a non-contact manner.

[0031] In the following examples, the terms first, second, etc. are not used in a limiting sense, but are used for the purpose of distinguishing one component from another.

[0032] In the examples below, singular expressions include plural expressions unless the context clearly indicates otherwise.

[0033] In the following examples, terms such as “include” or “have” mean that a feature or component described in the specification is present, and do not preclude the possibility that one or more other features or components may be added.

[0034] For convenience of explanation, the sizes of components in the drawings may be exaggerated or reduced. For example, the sizes and thicknesses of each component shown in the drawings are arbitrarily indicated for convenience of explanation, and thus the present invention is not necessarily limited to what is shown.

[0035] Additionally, terms such as “…part”, “…area”, etc. described in this specification may mean a unit that processes at least one function or operation.

[0036] Organoid culture can be utilized in therapeutic fields such as disease and cancer. Cultured organoids can be used to study characteristics such as tumor initiation, growth, and resistance. Cultured organoids can be used to study compounds with potential therapeutic targets for cancer cells through high-throughput drug screening. Cultured organoids can be used to establish patient-derived cancer cell lines and conduct tests such as drug sensitivity. Cultured organoids can also be used to study intratumoral heterogeneity.

[0037] Organoids, which are utilized in various ways as described above, can exhibit varying levels of gene expression in differentiated cells depending on changes in culture conditions. Because organoids can differentiate into different cell types depending on growth factors, concentrations, and media, culture conditions for organoids can include the composition of the culture medium, temperature, pH, oxygen concentration, growth factors, hormones, cytokines, media type, and the timing of factor administration.

[0038] In this specification, genomic data may include WES (Whole exome sequencing) and sequencing data, and may include data obtained by various analysis methods.

[0039] WES (Whole exome sequencing) sequences approximately 50 Mbp at approximately 100X, specifically obtaining only the exome region, and can obtain a relatively greater read depth than WGS (Whole genome sequencing). It is a method of analyzing only the genome of the exon region that directly codes for proteins. In the case of humans, exons account for less than 2% of the entire genome, so it is cheaper to acquire than WGS and the data capacity is also reduced. Even when analyzing WGS, variants in non-coding regions are difficult to interpret and may produce highly meaningful results. To solve this problem, analysis using WES is more economical and can obtain information at a higher depth.

[0040] RNA is an abbreviation for ribonucleic acid, and together they are called nucleic acids. Nucleic acids are polymers composed of multiple nucleotides. The nucleotides that make up DNA (deoxyribonucleic acid) are specifically called deoxyribonucleotides, and the nucleotides that make up RNA (ribonucleic acid) are specifically called ribonucleotides.

[0041] Here, a tumor refers to a swelling or wound where cells grow abnormally and metastasize. Examples of tumors include benign and malignant tumors. Tumor cells are cells that proliferate autonomously compared to normal cells, and have characteristics such as aneuploidy of chromosomes, abnormalities in cell membrane glycoproteins, loss of contact inhibition, specificity of cell membrane antigens, abnormalities in the biochemical properties of cells, and abnormalities in the enzyme system. Malignant tumor cells can refer to cancer cells, and can include liver cancer cells, stomach cancer cells, colon cancer cells, salivary gland cancer cells, skin cancer cells, lung cancer cells, uterine cancer cells, tonsil cancer cells, testicular cancer cells, brain metastasis cancer cells, laryngeal cancer cells, small intestine cancer cells, cervical cancer cells, breast cancer cells, ovarian cancer cells, head and neck cancer cells, pancreatic cancer cells, and thymus cancer cells.

[0042] Tumor organoids are three-dimensional structures that mimic certain characteristics of a tumor and may be derived from a patient's own tumor cells. Tumor organoids can be formed by culturing tumor cells in a specialized environment that mimics the conditions of the tumor microenvironment. Tumor organoids and their genomic data may include biological and genetic characteristics of the tumor.

[0043] FIG. 1 is a block diagram of an optimal organoid culture medium combination determination device (100, hereinafter, determination device) according to embodiments of the present disclosure.

[0044] The decision device (100) according to embodiments of the present disclosure can determine optimal culture medium conditions for organoids. The decision device (100) can determine optimal culture medium conditions by considering cells to be differentiated and the gene expression levels in the cells. A user may want to culture cells with a relatively high expression level of a specific gene. Conventionally, after culturing a large number of tumor cells to be used in research, research could be conducted with cells with a high gene expression level of the desired gene, or genes from other cells could be inserted into the cells using genetic scissors or the like. The decision device (100) according to embodiments of the present disclosure can output optimal culture medium conditions for organoids by utilizing an optimal condition determination model learned to determine culture medium conditions for culturing cells with a high gene expression level of the gene to be used in research.

[0045] The decision device (100) obtains, through various methods, result values ​​such as differentiation success rate, reproducibility, and gene expression level success rate of cells cultured with combinations of culture medium conditions input by a user, and generates a result distribution of result values ​​for a plurality of combinations of culture medium conditions to determine an optimal combination of culture medium conditions among the combinations of culture medium conditions. The success rate for gene expression level can be calculated as the degree to which the expression level of a gene matches a reference gene expression level. The decision device (100) can generate a result distribution by sampling result values ​​for combinations of culture medium conditions. The decision device (100) can determine an optimal combination of culture medium conditions for a given organoid through a process of updating the result values ​​for combinations of culture medium conditions to update the result distribution. The result value for the combination can be a value for cells cultured with culture medium conditions, and can be a value calculated by combining differentiation success rate, gene expression level success rate, and the like. The result value for the combination can be calculated using a preset mathematical formula for calculating the success rate of the division, the success rate of the gene expression level, etc. At this time, the result device (100) can update the result distribution using compensation for the Bayesian probability distribution.

[0046] The decision device (100) can obtain data on a combination of culture medium conditions for a given organoid and data on a gene from a user's input. Here, information on the gene can include a gene, a gene expression level, etc. The gene can be at least one of the genes expressed in the organoid. The gene expression level is a value for the expression level at which the corresponding gene should be expressed, and can include values ​​such as a specific value, above average, below average, twice the average, and half the average. The gene expression level input by the user can be referred to as a reference gene expression level. The user can obtain data on a combination of culture medium conditions that allow a specific gene to be expressed in the organoid at a desired reference gene expression level by determining an organoid to be cultured and a reference gene expression level of a specific gene through the decision device (100). The decision device (100) can generate a plurality of combinations of culture medium conditions by changing the concentration value of each growth factor based on the growth factors, which are conditions of the culture medium input by the user. At this time, the result distributions for the combinations of the first and fourth culture medium conditions input by the user are compared, and at least one of the concentration values ​​and types of growth factors in the combinations of the first and second culture medium conditions that satisfy specific conditions is utilized to generate a new combination of fifth culture medium conditions. Finally, a process of comparing the result distributions, which are the results of culture with the combinations of the first to fourth culture medium conditions and the newly generated fifth culture medium conditions, can be performed again. The culture medium conditions may include growth factors, concentration values ​​of the growth factors, information on supplements added during culturing, media, etc. Additionally, the culture medium conditions may further include temperature, pH, oxygen concentration, growth factors, hormones, cytokines, biochemical factors, etc.The culture medium conditions may further include numerical values ​​representing the degree, size, concentration, etc. of each of the above factors.

[0047] The decision device (100) can acquire the result values ​​of the results of culturing with a combination of culture medium conditions of the organoid from a culture experiment or a database, generate result distributions for each condition combination, and determine the optimal combination of culture medium conditions for the organoid using the generated result distributions. At this time, the result distributions for a plurality of combinations of culture medium conditions can be generated through a process of resampling using the result values ​​of additional culture experiments that are executed, predicted, or produced with each combination of culture medium conditions. The decision device (100) can determine the optimal combination of culture medium conditions for the organoid using an optimal condition determination model that is learned with the result values ​​of culture experiments executed with a combination of culture medium conditions. As mentioned above, the optimal condition determination model can be designed to output a new combination of culture medium conditions as an optimal combination of culture medium conditions in addition to the combinations of input culture medium conditions. Here, the combination of new culture medium conditions may be derived from various combinations, such as the average value of values ​​included in previously entered combinations, the weighted average value of values, etc.

[0048] The decision device (100) can pre-store information about the differentiation process for cells to be differentiated. Based on the information about the differentiation process, the decision device (100) can generate combinations of randomly set injection points of growth factors. Since each cell undergoes a predetermined process to differentiate into mature cells, information about the differentiation process is required to infer culture medium conditions that increase the differentiation success rate and gene expression levels of organoids.

[0049] An optimal condition determination model can be trained to output a distribution of results for culture medium conditions by taking as input a combination of conditions including information about organoids, growth factors, and concentration values ​​for the growth factors, and outputting results for culture medium conditions. The optimal condition determination model can be constructed to sample the distribution of results for each condition and output an optimal combination of conditions. The optimal decision model can periodically update the distribution of results for each culture medium condition by updating the input data.

[0050] At this time, the learned optimal condition decision model can output a combination of culture medium conditions that regulate the gene expression level of a specific gene by inferring factors of culture medium conditions that are highly correlated with cell differentiation success rate, reproducibility, and gene expression levels. The learned optimal condition decision model can learn factors of culture medium conditions that are correlated with each of one or more genes expressed in the cells. For example, the learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of MYC in cells such as Burkitt's lymphoma, breast cancer, lung cancer, and colon cancer. The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of HER2 in cells such as breast cancer and stomach cancer. The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of EGFR in cells such as non-small cell lung cancer, colon cancer, and head and neck cancer. The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of BRAF in cells such as malignant melanoma, colon cancer, and thyroid cancer. The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of Cyclin D1 in cells such as breast cancer, B-cell lymphoma, and esophageal cancer.

[0051] Information about organoids may include the type of organoid, the type of cell to be cultured (insulin-producing cells, retinal cells, fat cells, cardiac muscle cells, skin cells, bone cells, etc.), information related to the subject of the organoid (age, sex, etc.), information about mutations, the types of genes expressed in the organoid, and information about the expression level of each gene (average value, expression level at the time of cancer onset).

[0052] Examples of growth factors in culture medium conditions may further include VEGF (Vascular Endothelial Growth Factor), EGF (Epidermal Growth Factor), BMPs (Bone Morphogenetic Proteins), fatty acids, temperature, pH value, oxygen concentration, glucose concentration, lighting conditions, and concentration values. For example, organoid culture medium conditions for increasing the expression level of a specific gene may include BDNF (Brain-Derived Neurotrophic Factor).

[0053] As illustrated in FIG. 1, the decision device (100) according to some embodiments may include a processor (110), an input / output unit (130), a memory (140), a communication unit (150), and an optimal condition determination unit (120). However, not all of the components illustrated in FIG. 1 are essential components of the decision device (100). The decision device (100) may be implemented with more components than the components illustrated in FIG. 1, or with fewer components than the components illustrated in FIG. 1. The decision device (100) may be a user terminal, a server, a network system, or a separate device.

[0054] According to one embodiment of the present disclosure, the processor (110) typically controls the overall operation of the decision device (100). For example, the processor (110) may control the components included in the decision device (100) by executing a program stored in the decision device (100).

[0055] According to one embodiment of the present disclosure, the processor (110) can determine optimal culture medium conditions for organoids. The processor (110) can determine optimal culture medium conditions by considering the organoid to be cultured and the gene expression level in the organoid. The processor (110) can output optimal culture medium conditions for the organoids by utilizing an optimal condition determination model learned to determine culture medium conditions for culturing organoids expressed at a reference gene expression level of a gene to be utilized in research.

[0056] The processor (110) obtains, in various ways, the result values ​​such as the success rate of culture, reproducibility, gene expression level, and success rate of gene expression level of organoids cultured with combinations of culture medium conditions input by the user, and generates a result distribution of the result values ​​for a plurality of combinations of culture medium conditions to determine an optimal combination of culture medium conditions among the combinations of culture medium conditions. The processor (110) can generate a result distribution by sampling the result values ​​for combinations of culture medium conditions. The processor (110) can determine an optimal combination of culture medium conditions for a given organoid through a process of updating the result distribution by updating the result values ​​for the combinations of culture medium conditions. At this time, the processor (110) can update the result distribution by using compensation for the Bayesian probability distribution.

[0057] The processor (110) can obtain data on a combination of culture medium conditions for a given organoid and data on genes from a user's input. Here, the data on genes can include genes, reference gene expression levels, etc. The gene can be at least one of genes expressed in the organoid. The reference gene expression level is a value for the expression level at which the corresponding gene should be expressed, and can include values ​​such as a specific value, above average, below average, twice the average, and half the average. The user can obtain data on a combination of culture medium conditions that determine an organoid to be cultured and the gene expression level of a specific gene so that the specific gene is expressed in the organoid at the desired reference gene expression level through the determination device (100). The processor (110) can generate a plurality of combinations of culture medium conditions by changing the concentration value of each growth factor based on the growth factors, which are conditions of the culture medium input by the user. At this time, the result distributions for the combinations of the first and fourth culture medium conditions input by the user are compared, and the concentration values, injection times, chemical factors, etc. of the combinations of the first and second culture medium conditions that satisfy specific conditions can be used in various ways to generate a new combination of fifth culture medium conditions. Finally, a process of comparing the result distributions, which are the results of culture with the combinations of the first to fourth culture medium conditions and the newly generated fifth culture medium conditions, can be performed again. The culture medium conditions may include growth factors, concentration values ​​of the growth factors, information on supplements added during culturing, the injection timing (time point) of each growth factor, the medium, etc. In addition, the culture medium conditions may further include temperature, pH, oxygen concentration, growth factors, hormones, cytokines, biochemical factors, etc. The culture medium conditions may further include numerical values ​​representing the degree, size, concentration, etc. of each of the above factors.

[0058] The processor (110) can obtain the result values ​​of the results of culturing with a combination of culture medium conditions of the organoid from a culture experiment or a database, generate result distributions for each condition combination, and determine the optimal combination of culture medium conditions for the organoid using the generated result distributions. At this time, the result distributions for a plurality of combinations of culture medium conditions can be generated through a process of resampling using the result values ​​of additional culture experiments that are executed, predicted, or generated with each combination of culture medium conditions. The processor (110) can determine the optimal combination of culture medium conditions for the organoid using an optimal condition determination model that is learned with the result values ​​of culture experiments executed with a combination of culture medium conditions. As mentioned above, the optimal condition determination model can be designed to output a combination of newly generated culture medium conditions in addition to the combinations of input culture medium conditions as an optimal combination of culture medium conditions. A combination of new culture medium conditions can additionally generate a combination in which the concentration value is any value between the first concentration value and the second concentration value, if the results for the organoids cultured at the first concentration value and the second concentration value correspond to the first and second ranks among all the results. The arbitrary value can be determined by processing the first concentration value and the second concentration value by a method such as an average or a weighted average.

[0059] The processor (110) is a component for controlling the overall operation of the decision device (100). Specifically, the processor (110) controls the overall operation of the decision device (100) using various programs stored in the storage medium (150) of the decision device (100). For example, the processor (110) may include a CPU, RAM, ROM, and a system bus. Here, the ROM is a component in which a command set for system booting is stored, and the CPU copies the stored operating system of the decision device (100) to RAM according to the command stored in the ROM and executes the O / S to boot the system. When the system booting is complete, the CPU can copy various stored applications to RAM and execute them to perform various operations. Although the decision device (100) has been described as including only one CPU in the above, it may be implemented with multiple CPUs (or DSPs, SoCs, etc.).

[0060] According to one embodiment of the present invention, the processor (110) may be implemented as a digital signal processor (DSP), a microprocessor, or a time controller (TCON) that processes digital signals. However, the present invention is not limited thereto, and may include one or more of a central processing unit (CPU), a micro controller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), a communication processor (CP), or an ARM processor, or may be defined by the corresponding terms. In addition, the processor (110) may be implemented as a system on chip (SoC) or large scale integration (LSI) having a built-in processing algorithm, or may be implemented in the form of a field programmable gate array (FPGA).

[0061] According to one embodiment of the present disclosure, the input / output unit (130) can display an interface generated by the memory (140) of the decision device (100). According to one embodiment of the present invention, the input / output unit (130) can display a user interface for an input user input. The input / output unit (130) can output stored graphic data, visual data, auditory data, and vibration data under the control of the memory (140).

[0062] The input / output unit (130) may be implemented as a display panel of various forms. For example, the display panel may be implemented as a display technology of various forms, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), an active-matrix organic light-emitting diode (AM-OLED), a liquid crystal on silicon (LCoS), or a digital light processing (DLP). In addition, the input / output unit (130) may be coupled to at least one of the front area, the side area, and the rear area of ​​the display panel in the form of a flexible display.

[0063] The input / output unit (130) may be implemented as a touch screen with a layer structure. The touch screen may have a function of detecting not only a display function but also a touch input location, a touched area, and even a touch input pressure, and may also have a function of detecting not only a real touch but also a proximity touch.

[0064] The input / output unit (130) may include a user interface for inputting various information into the decision device (100).

[0065] According to one embodiment of the present disclosure, the memory (140) may store a program for processing and controlling the processor (110) and / or the optimal condition determination unit (120), and may also store data input to or output from the determination device (100). According to one embodiment of the present disclosure, the memory (140) may store information on genes, information on culture medium conditions of organoids, information on growth factors, generated result distributions, etc. The memory (140) may include a database storing the above information.

[0066] According to one embodiment of the present disclosure, the memory (140) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk. In addition, according to one embodiment of the present disclosure, programs stored in the memory (140) may be classified into a plurality of modules according to their functions.

[0067] According to one embodiment of the present disclosure, the communication unit (150) can communicate with an external device of the processor (110). For example, the communication unit (150) can communicate with an external device, such as a payment server or an authentication server, under the control of the processor (110). In addition, the communication unit (150) can also obtain user information or user input through communication with an external interface.

[0068] The optimal condition determination unit (120) may include a data input unit (121), an optimal combination output unit (122), a result update unit (123), and an output data generation unit (124), as illustrated in FIG. 2.

[0069] The data input unit (121) can obtain a combination of multiple organoid culture medium conditions, information about organoids, information about genes, and information about reference gene expression levels from user input. The information about genes may include at least one of the genes expressed in the organoids. The information about reference gene expression levels may include values ​​for the expression levels at which the corresponding genes should be expressed, such as a specific value, above average, below average, twice the average, half the average, etc. The combination of culture medium conditions may include growth factors, concentration values, medium, etc. For example, a combination of culture medium conditions may include growth factors such as CBM, Nicotinamide, hEGF, A83-01, SB202190, hGastrin I, Recombinant Human Noggin, R-SPO3, TGFB1, WRN conditioned medium, Wnt3a CM, Primocin, Y-27632, and concentration values ​​for each growth factor, as illustrated in FIG. 4.

[0070] The data input unit (121) can input data including combinations of multiple conditions and reference gene expression levels of genes. The gene expression level of a gene can include information about the gene and information about the gene expression level of the corresponding gene. The data input unit (121) can input data about combinations of conditions including growth factors. The data input unit (121) can input data about combinations of conditions including information about organoids and growth factors.

[0071] The optimal combination output unit (122) can input data including combinations of multiple organoid culture medium conditions, gene expression levels of genes, etc. into the optimal condition determination model, and sample the result distribution map for the gene expression levels of genes for the combinations. The optimal condition determination model can generate a large amount of data for the results of culturing with each combination of culture medium conditions. The optimal condition determination model can output the optimal condition combination based on the result data for each combination. The optimal condition determination model can compare the result distribution maps for the combinations of the first and fourth culture medium conditions input by the user, and can generate a new combination of fifth culture medium conditions by utilizing the concentration values ​​in the combination of the first and second culture medium conditions that satisfy a specific condition. Finally, a process of comparing the result distribution maps, which are the results of culturing with the combinations of the first to fourth culture medium conditions and the newly generated combination of the fifth culture medium conditions, can be performed again. Through the above additional process, the optimal condition determination model can output an optimal combination of conditions in which the genes in the organoid desired by the user are cultured at a set gene expression level.

[0072] The result update unit (123) can update the result distributions using compensation data including the result values ​​for the organoids cultured under the optimal condition combination in the optimal decision model. In other words, the result update unit (123) can input the result distribution for the newly added combination of the fifth culture medium conditions into the optimal decision model and process it to update the optimal decision model. The result update unit (123) can update the result distributions through random sampling based on the Bayesian probability distribution. The result values ​​for the organoids can include the culture success rate of the cultured organoids (number of successfully cultured organoids / total number of organoids), the match rate of oncogenic mutations with patient tissue, the reproducibility of the organoids (comparison of size, number, etc. with actual organoids, correlation coefficient), tumor purity, gene expression level values, etc. The result value for the organoids can be at least one of the gene expression level values ​​among the result values ​​for the organoids, the average value of the gene expression level values ​​for the organoids, and the combined value. Here, tumor purity can be determined using in-silico tumor purity estimation algorithms. More specifically, tumor purity can be calculated using DNA-seq-based methods such as ABSOLUTE, Sequenza, and FACETS, as well as RNA-seq (expression)-based methods such as ESTIMATE and PUREE.

[0073] Here, the cancer mutation match rate with patient tissue can be calculated based on mutant genes defined in a previously registered database. Examples of such databases include, but are not limited to, OncoKB and COSMIC DB.

[0074] The success rate and / or reproducibility of organoid culture can be input by the user based on the result data for the cultured organoids. The success rate and / or reproducibility of organoid culture can be calculated according to established rules, mathematical formulas, etc.

[0075] The result update unit (123) can update the optimal condition determination model to update the result distributions based on the compensation data.

[0076] The optimal combination output unit (122) can output an optimal combination of conditions in which a specific gene is cultured to have a set gene expression level in an organoid input by a user using an optimal condition determination model by a result update unit (123). The optimal condition determination model can output result distributions generated based on the gene expression level for each combination of input conditions. The Y-axis of the result distribution can be determined by the size of the gene expression level or the degree of matching with the gene expression level requested by the user. The X-axis of the result distribution can be determined by a value for the combinations. The optimal condition determination model can compare these result distributions and output an optimal combination of conditions having a gene expression level set by the user. The optimal combination output unit (122) can generate a new combination of conditions based on the result distributions for the combinations of input conditions. The optimal combination output unit (122) can remove the result value for the combination with the lowest result value (based on the X-axis) from the result distribution. The optimal combination output unit (122) can repeat the process of adding result values ​​for new combinations and deleting the combination with the lowest result value. The new combination of conditions can be determined by the optimal condition determination model. The optimal condition determination model can generate new combinations of conditions based on the correlation between the result values ​​of gene expression levels. Machine learning can be used to learn how to process the factors included in the input conditions.

[0077] The output data generation unit (124) can generate output data including the optimal condition combination from the optimal combination output unit (122).

[0078] The decision device (100) according to embodiments of the present disclosure can output an optimal condition combination from among combinations of multiple conditions devised by researchers and combinations of newly added conditions. When cultured using the optimal condition combination, the organoid input by the user can be cultured with a predetermined gene expression level. The decision device (100) can output a combination of culture medium conditions that includes optimal values ​​for concentration values ​​of growth factors of the conditions for the organoid. The decision device (100) can output the optimal condition combination using an optimal condition decision model that updates a result distribution by reapplying the result data obtained by experimentally culturing with the combination of conditions. The result distribution can be generated by sampling the result data for the combination of conditions. Methods for sampling the result data include, but are not limited to, Thomson Sampling, greedy methods, UCB (Upper Confidence Bound), Bayesian Optimization, Monte Carlo Sampling, and Bootstrapping, and various other sampling methods can be used. The result distribution can be generated by beta-distributing the result values ​​(gene expression levels of genes) for combinations of conditions. The result value can be calculated as the ratio of the actual cultured gene expression level value to the reference gene expression level.

[0079] Figure 3 is a block diagram of a learning device (300) that learns an optimal condition decision model.

[0080] The learning device (300) can train an optimal condition determination model. The learning device (300) can create a Gaussian process model that defines the relationship between combinations of culture medium conditions and gene expression levels, and train a model that outputs an optimal condition combination using a predetermined acquisition function. At this time, the acquisition function may include a function such as Upper Confidence Bound. The learning device (300) can train a model that outputs an optimal condition combination using a Bayesian optimization method. The trained optimal condition determination model can generate result distributions for each combination of input conditions using various sampling methods, and compare the result distributions to output an optimal condition combination. More specifically, the optimal condition determination model can generate result distributions using a Thomson sampling method. The trained optimal condition determination model may further include a module that generates a new combination of culture medium conditions using combinations of input culture medium conditions. The module that generates these combinations can generate new concentration values ​​based on the concentration values ​​of growth factors, or can generate them using combinations of two culture medium conditions in descending order of best results. The new combination of culture medium conditions can be generated to include at least one of a new concentration value, a new injection time point, and a new growth factor. The logic for generating a new combination of culture medium conditions can also be changed depending on the result distribution for the new combination of culture medium conditions. For example, if the result distribution for a new combination of culture medium conditions including the average value of the concentration values ​​has a result value greater than a predetermined reference value, a new combination of culture medium conditions can be generated by setting the average value of the concentration values.If the result distribution for a new combination of culture medium conditions has a result value higher than a predetermined reference value by removing a specific growth factor from the combination of culture medium conditions having the lowest result value, a new combination of culture medium conditions can be created by removing the specific growth factor.

[0081] The result distribution may be a two-dimensional data representation of the result values ​​cultured with each combination. The result distribution may be generated as two-dimensional data with the x-axis representing the success rate for the gene expression level of the gene and the y-axis representing the number of occurrences (density), as illustrated in Fig. 5a. The success rate for the gene expression level may be at least one of the ratio value of the actual gene expression level to the reference gene expression level and the occurrence ratio of the actual gene expression level within the reference range including the reference gene expression level. The result distribution may be expressed as a sampling of the result values ​​cultured with each combination. The result value for the organoid may include the expression success rate of the cultured organoid (the number of organoids that successfully expressed / the total number of organoids). The expression success rate may be a value for the probability that the gene is expressed at the expression level desired by the user. The y-axis value of the result distribution can be at least one of the result values ​​for the organoid, the average value of the result values ​​for the organoid, or a combined value. Here, the tumor purity can be a value determined using in-silico-based tumor purity estimation algorithms. More specifically, the tumor purity can be calculated using methods such as ABSOLUTE, Sequenza, and FACETS based on DNA-seq, and ESTIMATE and PUREE based on RNA-seq (expression). The result value can be calculated using values ​​such as the expression success rate and the tumor purity success rate using a preset method. The result value can be expressed as a success rate.

[0082] The dataset generation unit (310) can generate training data sets using combinations of conditions including information about organoids, information about genes (genes, gene expression levels), growth factors and concentration values ​​for the growth factors, and culture result values. For example, training data sets can be generated using combinations of four combinations of conditions for lung cancer cell organoids, gene expression levels for genes, and different concentration values ​​for growth factors for breast cancer cell organoids. The dataset generation unit (310) can generate training data sets using combinations of different growth factors depending on the type of organoid to be cultured and the desired gene expression levels.

[0083] The model learning unit (320) uses training data sets as input and generates, for each combination, distributions of results sampled using a sampling method such as Thompson sampling, and can train an optimal condition determination model using the distributions of results for each combination generated in this way.

[0084] The optimal condition determination model can output an optimal condition combination by inputting combinations of conditions, including information about organoids, growth factors, and concentration values ​​for those growth factors. Information about organoids can include expression levels of genes and reference genes to be expressed.

[0085] The model learning unit (320) can learn an optimal condition determination model that generates a result distribution using data obtained by performing a culture experiment with a combination of conditions input for the organoid.

[0086] The data obtained through the culture experiment may be data stored in a registered database and / or data experimentally obtained by the research team.

[0087] The model learning unit (320) can learn a model that statistically samples the results of gene expression levels in organoids cultured under conditions including growth factors, concentration values ​​for growth factors, etc., and generates a result distribution.

[0088] The model learning unit (320) can build an optimal condition determination model that compares the result distributions for each combination of conditions and outputs the optimal combination of conditions.

[0089] The model learning unit (320) can update the result distributions by changing the number of samplings performed for culture experiments based on a combination of conditions. For example, the number of samplings may increase as the training data set grows. Results from an increased number of samplings determine a combination of culture medium conditions for organoids with a higher success rate of gene expression compared to results from a lower number of samplings. The success rate of gene expression may be a value set based on whether the actual gene expression level matches the reference gene expression level of a given gene.

[0090] As shown in Fig. 5a, the higher the x-axis value at the peak point of the result distribution and the higher the y-axis value, the more optimal the combination of culture medium conditions can be. In Fig. 5a, condition 2 is the optimal condition combination. Through the arrangement of the result distribution, the expression success rate increases in the order of condition 2, condition 5, condition 3, condition 1, and condition 4 from the far right.

[0091] The model learning unit (320) can generate a combination of fifth culture medium conditions using combinations of the first to fourth culture medium conditions, and can generate a result distribution for the combination of the fifth culture medium conditions. As illustrated in Fig. 5b, a new condition 6 can be generated by combining condition 2 and condition 5. At this time, condition 2 and condition 5 may have the best expression success rate. According to the result distribution, considering the location of the peak, condition 2, which has the highest success rate and density, may be the optimal condition combination. The model learning unit (320) can generate a result distribution that compares combinations of the first to fifth culture medium conditions and eliminates a combination of culture medium conditions with the worst results. According to the result distribution of Fig. 5b, condition 1 may be eliminated. For the distribution of results for combinations of culture medium conditions that have the worst outcome, an optimal condition determination model can be trained to additionally perform the process of generating new culture medium conditions.

[0092] The model learning unit (320) can build an optimal condition determination model that outputs an optimal condition combination using the result distributions updated in this way.

[0093] The model learning unit (320) can periodically update the results of experimental cultivation using a combination of input conditions to update the result distribution.

[0094] The learning device (300) can generate an optimal condition decision model through the above learning process and transmit it to the decision device (100) to output an optimal condition combination.

[0095] Figure 4 is an exemplary diagram of combinations of culture medium conditions for organoid cells of lung cancer cells.

[0096] FIG. 5c is an exemplary diagram of a combination of new culture medium conditions generated according to embodiments of the present disclosure.

[0097] As shown in Fig. 5c, new combinations of culture medium conditions can be created by setting new values ​​of CBM, nicotinamide, hEGF, A83-01, SB202190, hGastrin I, Recombinant Human Noggin, R-SPO3, TGFB1, WRN conditioned medium, Wnt3a CM, Primocin, and Y-27632.

[0098] The combination of new culture medium conditions may include the average of two values ​​from the combination of two culture medium conditions that have the best gene expression success rate.

[0099] For example, the CBM value can be the average of the values ​​24.799, 49.649. The Nicotinamide value can be the average of 500, 500. The hEGF value can be the average of 50, 50. Since R-SPO3 is set for only one combination of culture medium conditions, that value can be included as is.

[0100] Figure 6 is a flowchart of an optimal condition output method according to embodiments of the present disclosure.

[0101] In S110, the decision device (100) can receive and acquire data including a combination of first culture medium conditions for a first organoid, a combination of second culture medium conditions, a combination of third culture medium conditions, and a reference gene expression level value of a first gene from a user. The combination of the first to third culture medium conditions may include one or more growth factors, concentration values ​​for each of the growth factors, a medium, etc. Additionally, the culture medium conditions may further include temperature, pH, oxygen concentration, growth factors, hormones, cytokines, biochemical factors, etc. The culture medium conditions may further include numerical values ​​representing the degree, size, concentration, etc. of each of the above factors. The medium information may include information on one or more growth factors included in the medium.

[0102] In S120, the decision device (100) can input combinations of first to third culture medium conditions and the first organoid into an optimal condition determination model to output a result distribution for combinations of the first to third culture medium conditions and one optimal condition combination among the combinations of the first to third culture medium conditions.

[0103] The optimal condition determination model can acquire the result data of experimental culture of the first organoid with a combination of first to third culture medium conditions through various routes, and can sample the result data acquired in this way to generate the first to third result distributions. The optimal condition determination model can compare the first to third result distributions to determine the result distribution with a higher result value (success rate of gene expression level) and determine the combination of culture medium conditions of the result distribution as the optimal condition combination and output it. The success rate of gene expression level can be a value that probabilistically calculates the degree of matching with a reference gene expression level. The gene expression level within a predetermined error range can be counted as the number of successful organoids. The optimal condition determination model can update the first to third result distributions by updating the result data of experimental culture of the first organoid. The optimal condition determination model can learn factors of culture medium conditions that are correlated with each of one or more genes expressed in the organoid. For example, the learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of MYC in organoids such as Burkitt's lymphoma, breast cancer, lung cancer, and colon cancer. The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of HER2 in organoids such as breast cancer and stomach cancer. The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of EGFR in organoids such as non-small cell lung cancer, colon cancer, and head and neck cancer. The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of BRAF in organoids such as malignant melanoma, colon cancer, and thyroid cancer.The learned optimal condition decision model can be trained to infer factors of culture medium conditions that are highly correlated with the gene expression level of Cyclin D1 in organoids such as breast cancer, B-cell lymphoma, and esophageal cancer.

[0104] In S130, the decision device (100) can output output data that visually displays the optimal condition combination for the first organoid. There may be two or more combinations of input culture medium conditions. If the reference gene expression level for the organoid changes, the output optimal condition combination may also change.

[0105] Through this, the method for determining optimal conditions according to embodiments of the present disclosure can determine a combination of culture medium conditions predicted to have optimal results (culture success rate, gene expression success rate, etc.) among several combinations of culture medium conditions suggested by a user. The method for determining optimal conditions can determine a combination of culture medium conditions predicted to have optimal results by updating the distribution of results of culture medium conditions by updating the results of experimental culture for the first organoid.

[0106] Figure 7 is a flowchart of an optimal condition output method according to other embodiments of the present disclosure.

[0107] In S210, the decision device (100) can generate a combination of fourth culture medium conditions using combinations of the first to third culture medium conditions.

[0108] The decision device (100) can compare the result distributions for combinations of the first and third culture medium conditions input by the user, and can generate a new combination of third culture medium conditions by variously utilizing concentration values, chemical factors, etc. in combinations of the first and second culture medium conditions that satisfy specific conditions. Finally, a process of comparing the result distributions, which are the results of culture using combinations of the first to third culture medium conditions and the newly generated combination of the fourth culture medium conditions, can be performed again.

[0109] The decision device (100) can compare the result distributions for combinations of the first and third culture medium conditions input by the user using the optimal condition determination model, and can generate a new combination of fourth culture medium conditions by utilizing the concentration values ​​in the combination of the first and second culture medium conditions that satisfy a specific condition. Finally, a process of comparing the result distributions resulting from culture with the combinations of the first to third culture medium conditions and the newly generated fourth culture medium conditions can be performed again. Through the above additional process, the optimal condition determination model can output an optimal combination of conditions in which a gene in an organoid desired by the user is cultured at a predetermined gene expression level.

[0110] In S220, the decision device (100) can generate result distributions for each combination of the first to fourth culture medium conditions, including a combination of the fourth culture medium conditions in which organoids are generated.

[0111] In this way, the decision device (100) can output an optimal condition combination among the combinations of culture medium conditions determined by itself by determining the concentration value for each growth factor, removal or insertion of chemical factors, etc. for inputs including growth factors.

[0112] In S230, the decision device (100) can output output data including the optimal condition combination output by the above process.

[0113] In addition, although the preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and various modifications can be made by a person having ordinary skill in the art to which the present invention pertains without departing from the gist of the present invention claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present invention.

[0114] Therefore, the spirit of the present invention should not be limited to the embodiments described above, and not only the scope of the patent claims described below, but also all scopes equivalent to or equivalently modified from the scope of the patent claims are considered to fall within the scope of the spirit of the present invention.

Claims

1. A step in which a decision device obtains data including growth factors for a first organoid, a combination of first culture medium conditions including concentration values ​​of the growth factors, a combination of second culture medium conditions, a combination of third culture medium conditions, and a reference gene expression level of a first gene from a user; The step of the above decision device inputting the combinations of the first to third culture medium conditions and information about the first organoid into the optimal condition decision model, comparing the result distributions for the combinations of the first to third culture medium conditions, and outputting the optimal condition combination based on the gene expression level of the first gene; and A method for determining an optimal combination by calculating gene expression levels for culture medium combinations, comprising: a step of outputting output data visually outputting an optimal condition combination in which the first organoid is cultured with the reference gene expression level of the first gene; 2. In paragraph 1, The above optimal condition decision model is, A method for determining an optimal combination by calculating gene expression levels for culture medium combinations, using a model trained using deep learning or machine learning, by inputting result distributions generated based on gene expression levels of the first gene in organoids cultured under a plurality of culture medium conditions and combinations of a plurality of culture medium conditions for a plurality of organoids.

3. In paragraph 2, The above optimal condition decision model is, A method for determining an optimal combination by calculating gene expression levels for combinations of culture media, generating a result distribution including the gene expression level of a first gene by sampling experimental results cultured under each combination of culture media conditions.

4. In paragraph 3, The above optimal condition decision model is, A method for determining the optimal combination by calculating the gene expression level for culture medium combinations, generating distribution maps of the results of sampling using the Thomson sampling method.

5. In paragraph 2, The above optimal condition decision model is, A method for determining an optimal combination by calculating the survival rate for combinations of culture media, wherein the method involves reinforcement learning through a process of relearning with result values ​​including the gene expression level value of the first gene obtained by increasing the number of sampling times for culturing the first organoid under the first to third culture media conditions.

6. In paragraph 1, The above result distribution is, A method for determining an optimal combination by calculating the gene expression levels for culture medium combinations, which visually represents the gene expression levels obtained for organoids cultured under a combination of culture medium conditions.

7. Including memory, communication unit, and processor, The above processor, Obtain data including growth factors for a first organoid from a user, a combination of first culture medium conditions including concentration values ​​of the growth factors, a combination of second culture medium conditions, a combination of third culture medium conditions, and a reference gene expression level of a first gene, By inputting the combination of the first to third culture medium conditions and information about the first organoid into the optimal condition determination model, the result distributions for the combinations of the first to third culture medium conditions are compared, and the optimal condition combination is output based on the gene expression level of the first gene. An optimal organoid culture medium combination determination device that outputs output data visually outputting an optimal condition combination in which the first organoid is cultured with the reference gene expression level of the first gene.

8. In paragraph 7, The above optimal condition decision model is, An optimal organoid culture medium combination determination device, which is a model learned using deep learning or machine learning, using as input the result distributions generated based on the gene expression level values ​​of the first gene in cells cultured under the plurality of culture medium conditions and combinations of the plurality of culture medium conditions for a plurality of organoids.

9. In paragraph 8, The above optimal condition decision model is, An optimal organoid culture medium combination determination device that samples experimental results cultured under each combination of culture medium conditions and generates a result distribution including the gene expression level of the first gene.

10. In paragraph 9, The above optimal condition decision model is, A device for determining the optimal organoid culture medium combination that generates distribution maps of sampling results using the Thompson sampling method.

11. In paragraph 8, The above optimal condition decision model is, An optimal organoid culture medium combination determination device, which is reinforced through a process of learning again using result values ​​including the gene expression level value of the first gene obtained by increasing the number of sampling times for culturing the first organoid under the first to third culture medium conditions.

12. In paragraph 7, The above result distribution is, A device for determining the optimal organoid culture medium combination, which visually represents the gene expression level values ​​obtained for organoids cultured under a combination of culture medium conditions.

13. A computer program stored in a computer-readable storage medium for executing any one of the methods of clauses 1 to 6 using a computer.

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