Modeling analysis method and system for tumor cell evolution, electronic device

Abstract

This invention relates to the field of information processing, providing a modeling and analysis method, system, and electronic device for tumor cell evolution. The method includes: constructing a cellular automaton model, which is a two-dimensional structure; setting an immune microenvironment within the cellular automaton model, the immune microenvironment including immune T cells, macrophages, and nutritional conditions; and simulating and predicting the evolution of tumor cells within the immune microenvironment under preset conditions using the cellular automaton module, and recording the evolution process of tumor cells in the cellular automaton model. This application can be used to study the evolution of tumor cells in non-small cell lung cancer patients under the interaction of tumor cells and immune cells. Furthermore, the supply and metabolism of nutrients have a certain impact on both tumor cells and immune cells; therefore, analyzing the interaction between tumor cells and the immune system and nutrients is of great significance for understanding the occurrence, development, and treatment of cancer.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a modeling and analysis method and system for tumor cell evolution, as well as electronic equipment. Background Technology

[0002] The evolution of tumor cells is a dynamic process involving multi-scale and multi-factor interactions, encompassing the entire life cycle from single-cell gene mutations to overall tumor growth, invasion, and metastasis. Tumor cells can not only proliferate and metastasize rapidly, but also interact with immune cells, stromal cells, fibroblasts, vascular endothelial cells, and other tissue-resident cellular components. Therefore, analyzing and simulating the evolution of tumor cells in the immune microenvironment is helpful for the inhibition and elimination of tumor cells.

[0003] In existing technologies, various mathematical frameworks, including ordinary differential equations and partial differential equations (ODEs and PDEs), have been proposed to predict tumor cell development and implement personalized treatment strategies. However, they are often insufficient in capturing cell heterogeneity and discrete space, as well as simulating intercellular interactions. Furthermore, they are difficult to simultaneously capture the dynamics of local cell behavior and global tissue, making it difficult to accurately simulate the evolution of tumor cells and affecting research on tumor cells.

[0004] On the other hand, the above methods often fail to accurately capture the discrete interactions between cells and the spatiotemporal heterogeneity of the microenvironment. In contrast, cellular automata (CA) models (which treat cells as discrete entities on a grid) have significant advantages. Specifically, CA methods can: (1) provide more biologically grounded simulations of individual cell behavior; (2) present the spatiotemporal distribution of cells more clearly; and (3) more accurately simulate the interactions between adjacent cells in a spatially structured microenvironment. Summary of the Invention

[0005] This invention provides a modeling and analysis method, system, and electronic device for tumor cell evolution. It can be used to analyze tumor cell evolution under conditions of interaction between tumor cells and immune cells, considering tumor treatment, adequate nutrient supply, and metabolic processes. Furthermore, nutrient supply and metabolism both influence tumor cells and immune cells. Therefore, analyzing the interactions between tumor cells, the immune system, and nutrients is crucial for understanding the occurrence, development, and treatment of tumor cells.

[0006] This invention provides a method for modeling and analyzing tumor cell evolution, comprising: Construct a cellular automaton model, wherein the cellular automaton model is a two-dimensional structure; An immune microenvironment is set up in the cellular automata model, which includes immune T cells, macrophages, and nutritional conditions; Under preset conditions, the evolution of tumor cells in the immune microenvironment is simulated and predicted by the cellular automata module, and the evolution process of tumor cells in the cellular automata model is recorded.

[0007] According to the tumor cell evolution modeling and analysis method provided by the present invention, the immune T cells include cytotoxic T cells, regulatory T cells, and helper T cells; The macrophages include M1 macrophages and M2 macrophages.

[0008] According to the tumor cell evolution modeling and analysis method provided by the present invention, the step of simulating and predicting the evolution of tumor cells in the immune microenvironment under preset conditions through the cellular automata module includes: The tumor cells are placed at the center of the cellular automata model; The immune T cells and the macrophages are deployed to the periphery of the cellular automata model.

[0009] According to the tumor cell evolution modeling and analysis method provided by the present invention, the step of setting up an immune microenvironment in the cellular automata model includes: The differentiation rate of tumor cells in the cellular automata model is set. The cellular heterogeneity of tumor cells and cytotoxic T cells in the cellular automata model is configured. The proliferation probability, death probability, and migration probability of tumor cells, immune T cells, and macrophages in the cellular automata model are set.

[0010] According to the tumor cell evolution modeling and analysis method provided by the present invention, the step of setting up an immune microenvironment in the cellular automata model further includes: The nutritional conditions in the cellular automata model are set.

[0011] According to the modeling and analysis method for tumor cell evolution provided by the present invention, the preset conditions include factors influencing tumor cell evolution; The process of simulating and predicting the evolution of tumor cells in the immune microenvironment using the cellular automata module under preset conditions includes: A control experiment was set up based on the aforementioned influencing factors. The evolution process of tumor cells was simulated using the cellular automata model, and the evolution process was recorded.

[0012] According to the present invention, the method and system for analyzing the evolution of tumor cells in the immune microenvironment include influencing factors such as treatment method, treatment time, nutritional supply, and whether or not there is immune cell interference.

[0013] This invention also provides a modeling and analysis system for tumor cell evolution, which applies methods and systems for analyzing the evolution of tumor cells in the immune microenvironment, including: The model building module is used to build a cellular automaton model, which is a two-dimensional structure. A model initialization module is used to set up an immune microenvironment in the cellular automata model, wherein the immune microenvironment includes immune T cells and macrophages; The cell evolution simulation module is used to simulate and predict the evolution of tumor cells in the immune microenvironment under preset conditions through the cellular automata module, and to record the evolution process of tumor cells in the cellular automata model.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement any of the above-described methods for modeling and analyzing tumor cell evolution.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for modeling and analyzing tumor cell evolution.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a modeling and analysis method for tumor cell evolution as described above.

[0017] The tumor cell evolution modeling and analysis method provided by this invention can combine a cellular automata model to model and analyze tumor cell evolution. Since each cell in the cellular automata model has different parameters according to the model's coordination and parameters, the model is discrete in both time and space. Thus, the changes of tumor cells in space and time can be accurately simulated, which facilitates the recording and analysis of the tumor cell evolution process. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1This is a schematic flowchart of the modeling and analysis method for tumor cell evolution provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the cellular automata model provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of intercellular interactions provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the principle of tumor cell evolution provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the cell state transformation process of tumor stem cells provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the cell state transformation process of tumor daughter cells provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the cell state transition process of cytotoxic T cells provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the cell state transition process of regulatory T cells provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the cell state transition process of helper T cells provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the cell state transition process of M1 macrophages provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the cell state transition process of M2 macrophages provided in an embodiment of the present invention; Figure 12 This is one of the schematic diagrams of tumor cell evolution simulation results provided in the embodiments of the present invention; Figure 13 This is the second schematic diagram of the simulation results of tumor cell evolution provided in the embodiments of the present invention; Figure 14 This is the third schematic diagram of the tumor cell evolution simulation results provided in the embodiments of the present invention; Figure 15 This is the fourth schematic diagram of the tumor cell evolution simulation results provided in the embodiments of the present invention; Figure 16 This is the fifth schematic diagram of the tumor cell evolution simulation results provided in the embodiments of the present invention; Figure 17 This is a schematic diagram of the structure of the tumor cell evolution modeling and analysis system provided in an embodiment of the present invention; Figure 18 This is a schematic diagram of the physical structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] Figure 1 This is a schematic flowchart of the modeling and analysis method for tumor cell evolution provided in an embodiment of the present invention.

[0022] like Figure 1 As shown, this embodiment provides a modeling and analysis method for tumor cell evolution, including: Step 101: Construct a cellular automaton model, wherein the cellular automaton model is a two-dimensional structure; Cellular automata are discrete dynamical models where time, space, and state are all discrete. They drive global evolution through local rules. The model consists of a rule-defined grid system where each cell updates its own state synchronously based on the states of its neighbors, enabling the simulation of the spatiotemporal evolution of complex systems. Core elements include cells, cell space, neighbors, and evolutionary rules. Applications include group behavior simulation, traffic flow analysis, materials science, and parallel computing.

[0023] Specifically, the cellular automata model is a stochastic framework in which different cells generated by a single cell (factor) can have different characteristics depending on the model configuration and parameters. Therefore, the model is discrete in both time and space. Cell-based or discrete models allow for tracking the spatial and temporal variations of completely independent individual parameters, reflecting the heterogeneity and complexity of cancer phenomena. This facilitates consideration of spatial and phenotypic heterogeneity.

[0024] Figure 2 This is a schematic diagram of the cellular automata model provided in an embodiment of the present invention.

[0025] like Figure 2As shown, the cellular automaton model in this embodiment can be a two-dimensional space, with each cell having six neighbors. Therefore, the cellular automaton in this embodiment consists of many hexagons, where each cell can hold at most one element at a time. Mole's neighborhood is considered here, meaning that any cell can proliferate, migrate, or die within a computational time step (1 day). Newly proliferated cells occupy an empty position adjacent to the parent cell, while another new cell occupies the original position. Migrating or differentiated cells occupy an empty position adjacent to the original cell. When there are no empty positions (i.e., empty spaces) around a cell, the cell cannot proliferate (unless a cell has died), and thus remains in a static state. All cell state transitions have random probabilities. The random process works by drawing a random number between 0 and 1 for each cell in each round. If this number is less than the probability of a cell state transition, the transition is executed.

[0026] Step 102: Set up an immune microenvironment in the cellular automata model, the immune microenvironment including immune T cells, macrophages and nutritional conditions; In practice, the cell types in the cell set include tumor cells, immune T cells, and macrophages. Specifically, tumor cells can include tumor stem cells and tumor daughter cells. Tumor stem cells lack cell death mechanisms and have unlimited proliferative capacity; regardless of how many times they divide, they will continue to proliferate, although their proliferation rate is very low. Furthermore, differentiation into tumor cells may occur during division. Tumor daughter cells have a higher proliferation rate, but their number of proliferations is limited; they die after producing a finite number of progeny cells. Immune T cells are the body's main immune cells, capable of recognizing and eliminating tumor cells, thereby effectively inhibiting tumor growth and spread. Immune T cells are mainly divided into cytotoxic T cells (Tc), regulatory T cells (Tr), and helper T cells (Th). Macrophages are important immune cells in the body, playing crucial roles in anti-infection, anti-tumor, and immune regulation. Macrophages limit the spread and diffusion of pathogens through apoptosis. There are two types: M1 macrophages and M2 macrophages. M1 macrophages have anti-tumor effects, while M2 macrophages have pro-tumor effects.

[0027] The reason why the above-mentioned cell types were chosen as the cell set in this embodiment to simulate and predict the evolution of tumor cells is that these cell types all interact with the evolution of tumor cells. Specifically, for example... Figure 3 As shown, tumor stem cells (CSTs) may proliferate into tumor stem cells or differentiate into tumor daughter cells (C) during division. Tumor cells may die after a limited number of proliferations.

[0028] M1 macrophages can kill tumor cells or tumor stem cells when they are near them, have maximum killing power before being depleted, and can die spontaneously. M2 macrophages promote tumor growth and can also die spontaneously. Furthermore, these two types of cells can interconvert.

[0029] Cytotoxic T cells (Tc) are a key component of the immune system, primarily responsible for recognizing and killing tumor cells. When tumor cells express PD-L1 (an immunosuppressive molecule), they inhibit the tumor-killing effect of Tc. Cancer stem cells recruit regulatory T cells (Tr) by secreting chemokines or promote Tr proliferation by secreting cytokines to remodel the metabolic environment, but inhibit the proliferation of helper T cells (Th). Tr plays a crucial role in suppressing the proliferation of both Tc and Th. Th effectively promotes Tc proliferation. All three types of T cells proliferate into their own cell type and eventually die.

[0030] Both tumor stem cells and Tr can promote the transformation of M1 to M2, while Tc and Th can promote the transformation of M2 to M1.

[0031] Intercellular interaction diagram as follows Figure 1 As shown. Arrows indicate positive regulation. Blocked arrows indicate negative regulation. Positive regulation increases the probability of cell proliferation, while negative regulation increases the probability of cell death.

[0032] In practical applications, when cells are deployed into the cellular automaton model, the initial deployment positions of tumor stem cells and ordinary tumor cells can be set in the middle of the cellular automaton, while the initial deployment positions of immune T cells and macrophages can be set outside the tumor cells. This simulates the migration of immune T cells and macrophages from the lymph nodes to the tumor site when tumor cells appear in the organism, thereby killing the tumor cells.

[0033] It should be noted that the above examples are only illustrations of cell deployment methods and are not intended to limit the scheme of this application. The actual deployment location of cells can also be flexibly selected according to actual needs.

[0034] In practical applications, the distribution can be carried out at a set ratio. Specifically, Table 1 below provides one implementation method: Table 1 Initial Cell Ratio Table

[0035] It can be seen that the proportion of cells deployed is determined based on the proportion of cells in the blood of real organisms, thus enabling a more realistic simulation of the evolution process of tumor cells.

[0036] Step 103: Under preset conditions, the evolution of tumor cells in the immune microenvironment is simulated and predicted by the cellular automata module, and the evolution process of tumor cells in the cellular automata model is recorded.

[0037] In practical applications, the immune microenvironment can also include the nutrition of the immune environment in relation to the treatment methods for tumor cells. Therefore, in practical applications, the preset conditions in this step can include various influencing factors that affect the evolution of tumor cells. For example, they can include treatment methods, treatment time, nutritional supply, whether or not there is immune cell intervention, etc.

[0038] Adequate nutritional intake is essential for the proper functioning of the immune system, particularly through nutrients such as protein, essential fatty acids, and vitamins, which are crucial for maintaining the normal function of immune cells. Nutritional deficiencies can lead to a decline in immune system function. For example, protein deficiency can reduce the number of lymphocytes, affecting cellular immunity and reducing the body's ability to recognize and eliminate tumor cells. Adequate nutrition also helps patients tolerate more intensive treatments (such as chemotherapy or radiotherapy), which often have a strong killing effect on tumor cells but also produce side effects on normal cells. Patients with good nutritional status are better able to tolerate these side effects, thus reducing the likelihood of treatment interruption and maintaining the continuity and intensity of treatment.

[0039] Nutritional status is heterogeneous and changes dynamically over time. In this embodiment, in addition to exploring the inhibitory effect of immune cells (including immune T cells and macrophages) on tumor growth, the effects of treatment, nutrients, and catabolism on cell growth, as well as the tumor growth process after recurrence following treatment, were also considered. We primarily considered single therapies of targeted therapy and immunotherapy, while also considering the effects of combined use of these two therapies. The influence of different nutritional conditions on cell growth was also taken into account.

[0040] Specifically, such as Figure 4 As shown, Figure 4In the cellular automaton, I, T, H, and O represent immune cells, tumor cells, treatment methods, and nutrients, respectively. Arrows indicate the promotion of the corresponding cell's proliferation, while arrows with blocking symbols indicate the promotion of the corresponding cell's death or the inhibition of nutrient absorption. To simulate this dynamic evolution process, each grid point in the cellular automaton can be assigned a continuous variable O, with a value between 50 and 100, to characterize its local nutritional status. This value also changes over time. Nutrients within each grid can diffuse to the surrounding network and be consumed by different types of cells, while also being supplied by external nutrient sources. Nutrients drive cell growth and movement. The nutritional status changes at positions i and j in the cellular automaton consist of two parts: the supply of nutrients and the consumption of nutrients by the cells.

[0041] In practical applications, control experiments can be set up based on the aforementioned influencing factors, and the evolution process of tumor cells can be simulated and recorded using the cellular automata model.

[0042] In an exemplary embodiment, in order to simulate the process of tissue growth and change, the cellular automata model of this application incorporates key cell behavior elements, including proliferation, differentiation, migration, and death. In general, each cell has a random probability of state transition at each stage. The rule is that each cell will randomly draw a value between 0 and 1. If the value is less than the probability of cell state transition, the cell will undergo a state transition. The state of each cell in the next stage depends on its current state, the state of its six neighborhoods, and the local nutrient conditions. In this way, the state of each cell in the cellular automata model can be updated, and the evolutionary dynamics of all cells can be simulated.

[0043] Cell proliferation, differentiation, migration, and death are cellular behaviors. In addition, when none of the above cellular behaviors occur, the cell is in a quiescent state. In the scheme of this application, the cellular automata model can not only simulate the above cellular behaviors, but also simulate the interactions between cells, such as the promoting or inhibiting effects between cells and the application of the nutritional environment to cells.

[0044] The following section provides a detailed overview of the cellular state transitions between tumor cells, immune T cells, and macrophages during the evolution of tumor cells.

[0045] Figure 5 This is a schematic diagram of the cell state transformation process of tumor stem cells provided in an embodiment of the present invention.

[0046] like Figure 5As shown, if the cell is a tumor stem cell, a random number between 0 and 1 is first generated. It is then determined whether this random number is less than or equal to the proliferation probability. If it is less than the probability and there is space around the cell, it is divided into two cells, each with a certain probability of becoming a tumor cell or a tumor stem cell. If this random number is less than or equal to the sum of the proliferation probability and the resting-phase differentiation probability, and is greater than the proliferation probability, then this tumor stem cell will differentiate into a tumor cell. If this random number is less than or equal to the sum of the proliferation probability, the resting-phase differentiation probability, and the migration probability, and is greater than the aforementioned probabilities, and there is space around the cell, then this tumor stem cell will migrate. If this random number is greater than the aforementioned probability, then the tumor stem cell remains in its original state.

[0047] Figure 6 This is a schematic diagram of the cell state transformation process of tumor daughter cells provided in an embodiment of the present invention.

[0048] If the cell is a tumor daughter cell, first generate a random number between 0 and 1, and determine whether this random number is less than or equal to the proliferation probability. If it is less than or equal to and there is space around the cell, then it is transformed into two tumor daughter cells. Otherwise, further determine whether this random number is less than or equal to the sum of the proliferation probability and the given death probability. If it is less than or equal to, then the tumor daughter cell dies. If this random number is less than or equal to the sum of the proliferation probability, the death probability, and the migration probability, and there is space around the cell, then the cell migrates. Otherwise, the tumor daughter cell remains in its original state.

[0049] Figure 7 This is a schematic diagram of the cell state transition process of cytotoxic T cells provided in an embodiment of the present invention.

[0050] If the cell is a cytotoxic T cell, first generate a random number between 0 and 1. Determine if this random number is less than or equal to the proliferation probability. If it is less than or equal to and there is space around the cell, then it is converted into two cytotoxic T cells. Otherwise, further determine if this random number is less than or equal to the sum of the proliferation probability and the given death probability. If it is less than or equal to, then the cytotoxic T cell dies. If this random number is less than or equal to the sum of the proliferation probability, the death probability, and the migration probability, and there is space around the cell, then the cell migrates. Otherwise, the cytotoxic T cell remains in its original state.

[0051] Figure 8 This is a schematic diagram of the cell state transition process of regulatory T cells provided in an embodiment of the present invention.

[0052] If the cell is a regulatory T cell, first generate a random number between 0 and 1. Determine if this random number is less than or equal to the proliferation probability. If it is less than or equal to and there is space around the cell, then it is converted into two regulatory T cells. Otherwise, further determine if this random number is less than or equal to the sum of the proliferation probability and the given death probability. If it is less than or equal to, then the regulatory T cell dies. If this random number is less than or equal to the sum of the proliferation probability, the death probability, and the migration probability, and there is space around the cell, then the regulatory T cell migrates. Otherwise, the regulatory T cell remains in its original state (same as above).

[0053] Figure 9 This is a schematic diagram of the cell state transition process of helper T cells provided in an embodiment of the present invention.

[0054] If the cell is a helper T cell, first generate a random number between 0 and 1. Determine if this random number is less than or equal to the proliferation probability. If it is less than or equal to and there is space around the cell, then it is converted into two helper T cells. Otherwise, further determine if this random number is less than or equal to the sum of the proliferation probability and the given death probability. If it is less than or equal to, then the helper T cell dies. If this random number is less than or equal to the sum of the proliferation probability, the death probability, and the migration probability, and there is space around the cell, then the helper T cell migrates. Otherwise, the helper T cell remains in its original state (same as above).

[0055] Figure 10 This is a schematic diagram of the cell state transition process of M1 macrophages provided in an embodiment of the present invention.

[0056] If the cell is an M1 macrophage, a random number between 0 and 1 is generated first. The program then checks if this random number is less than or equal to the probability of death. If it is, the M1 macrophage dies. If the random number is less than or equal to the sum of the probability of death and the probability of migration, and there is space around the cell, the M1 macrophage migrates. If the random number is less than or equal to the sum of the probability of death, the probability of migration, and the conversion rate from M1 to M2, the cell transforms into an M2 macrophage. Otherwise, the M1 macrophage remains in its original state. Since M1 macrophages do not have proliferative capacity, they are periodically replenished in the program.

[0057] Figure 11 This is a schematic diagram of the cell state transition process of M2 macrophages provided in an embodiment of the present invention.

[0058] If the cell is an M2 macrophage, a random number between 0 and 1 is generated first. The program then checks if this random number is less than or equal to the probability of death. If it is, the M2 macrophage dies. If the random number is less than or equal to the sum of the probability of death and the probability of migration, and there is space around the cell, the M2 macrophage migrates. If the random number is less than or equal to the sum of the probability of death, the probability of migration, and the conversion rate from M2 to M1, the cell transforms into an M1 macrophage. Otherwise, the M2 macrophage remains in its original state. Since M2 macrophages do not have proliferative capacity, they are periodically replenished in the program.

[0059] In an exemplary embodiment, a proliferative heterogeneity index Xcst ∈ [0, 1] can be introduced to represent the proliferative capacity level of tumor stem cells. The closer Xcst is to 1, the stronger the proliferative capacity of tumor stem cells; the closer Xcst is to 0, the weaker the proliferative capacity of tumor stem cells. The immunosuppressive level of tumor stem cells can also be represented by a heterogeneity index Hcst ∈ [0, 1]. The closer Hcst is to 1, the stronger the immunosuppressive capacity of tumor stem cells; the closer Hcst is to 0, the weaker the immunosuppressive capacity of tumor stem cells. In other words, a larger Hcst also means that tumor stem cells highly express immune checkpoints, thus exhibiting stronger inhibition of helper T cells, and these types of tumor stem cells will be more sensitive to treatment with immune checkpoint inhibitors. A drug resistance index Fcst ∈ [0, 1] is also set for tumor stem cells. The closer Fcst is to 1, the stronger the response of tumor stem cells to drugs; the closer Fcst is to 0, the weaker the response of tumor stem cells to drugs.

[0060] When simulating and predicting the heterogeneity of tumor daughter cells, the proliferative capacity of tumor cells can be represented by the index Xc ∈ [0, 1]. Xc approaching 1 indicates stronger proliferative capacity, while Xc approaching 0 indicates weaker proliferative capacity. The immunosuppressive level of tumor cells is represented by Hc ∈ [0, 1]. Hc approaching 1 indicates stronger immunosuppressive capacity, while Hc approaching 0 indicates weaker immunosuppressive capacity. In other words, a larger Hc indicates higher expression of immune checkpoints in tumor cells, leading to stronger suppression of helper T cells, and these tumor cells are more sensitive to treatment with immune checkpoint inhibitors. A targeted therapy resistance index Fc ∈ [0, 1] is also set for tumor cells. Fcst approaching 1 indicates a stronger response of tumor stem cells to drugs, while Fcst approaching 0 indicates a weaker response.

[0061] In practice, the proliferation capacity of cytotoxic T cells can be represented by the index XTc ∈ [0, 1]. XTc close to 1 indicates stronger proliferation capacity of cytotoxic T cells, while XTc close to 0 indicates weaker proliferation capacity. The heterogeneity index HTc ∈ [0, 1] can be used to reflect the differences in their killing ability, representing the killing level of cytotoxic T cells against tumor (stem) cells (the parameters of these two are different, making the killing level of cytotoxic T cells against tumor cells stronger than that against tumor stem cells). A larger HTc indicates a higher killing level, and a smaller HTc indicates a lower killing level. This value is fixed and does not change over time.

[0062] In addition to the heterogeneity parameters mentioned above, the scheme of this application also provides a detailed description of the proliferation probability, death probability and migration probability of various types of cells in the cell set. That is, the scheme of this application realizes the simulation of cell behavior through the following formula, as shown below.

[0063] When setting the proliferation probability, the tumor daughter cell includes its own proliferation probability and the M2 macrophage-mediated proliferation probability, specifically conforming to the following formula: (1) (2) (3) Among them, in the above formulas (1) to (3) This represents the maximum proliferation rate of the tumor daughter cells themselves. , and These are all parameters; x represents an indicator of proliferative heterogeneity in tumor daughter cells. M2 macrophage-mediated tumor cell proliferation rate The proportion of M2 macrophages surrounding the tumor daughter cells. The effect of tumor daughter cell proliferation heterogeneity indicators on tumor daughter cell proliferation probability. The coefficient representing the effect of nutrition on the proliferation rate of tumor cells.

[0064] For tumor stem cells, the formula for calculating their proliferation probability is the same as that for tumor daughter cells, except... , as well as The values ​​are different.

[0065] For cytotoxic T cells, their proliferation probability includes their own proliferation probability and the proliferation probability mediated by helper T cells, which conforms to the following formula: (4) (5) (6) Among them, in formulas (4) to (6) above This represents the maximum proliferation rate of cytotoxic T cells themselves. This refers to the proliferation rate of cytotoxic T cells mediated by helper T cells. , and These are all parameters; x represents an indicator of the proliferative heterogeneity of cytotoxic T cells. This refers to the proportion of helper T cells surrounding cytotoxic T cells. The effect of cytotoxic T cell proliferation heterogeneity indicators on cell proliferation probability. The coefficient representing the effect of nutrition on the proliferation rate of cytotoxic T cells.

[0066] For regulatory T cells, their proliferation probability includes their own proliferation probability and the proliferation probability mediated by tumor daughter cells, which conforms to the following formula (7): (7) Among them, in formula (7) This represents the maximum proliferation rate of regulatory T cells themselves. The rate of regulatory T cell proliferation mediated by tumor daughter cells. This refers to the proportion of tumor daughter cells surrounding regulatory T cells. The coefficient representing the effect of nutrition on the proliferation rate of regulatory T cells.

[0067] For helper T cells, their proliferation probability only involves their own proliferation probability, which conforms to the following formula (8): (8) In the above formula (8) This represents the maximum proliferation rate of regulatory T cells themselves. The coefficient representing the effect of nutrition on the proliferation rate of regulatory T cells.

[0068] Since macrophages themselves do not proliferate, their proliferation probability can be represented by the transformation rate from M2 macrophages to M1 macrophages, including the transformation rate from M2 macrophages to M1 macrophages, as well as the transformation rate mediated by helper T cells and cytotoxic T cells, which conforms to the following formula (9): (9) in, The conversion rate of M2 macrophages to M1 macrophages. and This refers to the proportion of helper T cells and cytotoxic T cells in the outer ring of M2 macrophages. The effect of helper T cells on the transformation rate of M2 macrophages to M1 macrophages. The effect of cytotoxic T cells on the transformation rate of M2 macrophages to M1 macrophages.

[0069] The transformation rate from M1 macrophages to M2 macrophages can include the transformation rate from M1 macrophages to M2 macrophages, as well as the transformation rates mediated by tumor stem cells, tumor daughter cells, and regulatory T cells, and conforms to the following formula (10): (10) in, The conversion rate of M1 macrophages to M2 macrophages. , and This refers to the proportion of tumor daughter cells, regulatory T cells, and tumor stem cells in the outer ring of M1 macrophages. The effect of tumor daughter cells on the transformation rate of M1 macrophages to M2 macrophages. The effect of regulatory T cells on the transformation rate of M1 macrophages to M2 macrophages. The effect of tumor stem cells on the transformation rate of M1 macrophages to M2 macrophages.

[0070] In practical applications, when setting the mortality probability, the mortality probability of tumor stem cells includes the mortality probability mediated by cytotoxic T cells and M1 macrophages, as well as the mortality probability due to treatment.

[0071] The probability of death of tumor daughter cells includes their own death probability, the death probability mediated by cytotoxic T cells and M1 macrophages, and the death probability due to treatment.

[0072] The probability of death of cytotoxic T cells includes their own mortality probability and the mortality probability mediated by regulatory T cells.

[0073] The probability of regulatory T cell death includes only the probability of its own death.

[0074] The death probability of helper T cells includes their own death probability as well as the death probability mediated by regulatory T cells and tumor daughter cells.

[0075] The death probability of M1 macrophages only includes their own death probability.

[0076] The death probability of M2 macrophages only includes their own death probability.

[0077] When setting migration probabilities, the migration probabilities are similar for all cell types. The migration probability of tumor daughter cells is used as an example below, conforming to the following formula: in, This represents the migration probability of tumor daughter cells themselves. The migration probability of tumor daughter cells mediated by spatial information. The proportion of the space surrounding the tumor daughter cells.

[0078] In an exemplary embodiment, simulating and predicting the evolution of tumor cells in the immune microenvironment using the cellular automata module may include the following steps: (1) Initialization.

[0079] 1.1 Update the cell type, cell state, and cell ratio in the cellular automaton. For example, you can refer to Table 1 for initialization. You can also initialize the nutrient value corresponding to each grid. 1.2 Initialize the spatial structure of the cellular automaton and set the initial position of each cell. For example, tumor stem cells and tumor daughter cells can be placed in the center of the cellular automaton with some empty spaces around them, and other types of cells can be placed on the periphery. Initial heterogeneity indicators can also be set. 1.3 Calculate the state transition rate of each cell and the changes in nutrient conditions for each grid cell; (2) Update: Update each cell and grid location at each simulation time step.

[0080] 2.1 Differentiation: Tumor stem cells may differentiate into tumor daughter cells; 2.2. Division: Each cell divides with a certain probability; 2.3 Death: Except for tumor stem cells, all other cells may die with a certain probability. 2.4 Migration: Each cell migrates with a certain probability. 2.5. Heterogeneity index values: Update the heterogeneity index values ​​for tumor stem cells, tumor daughter cells, and cytotoxic T cells; 2.6 Nutrient Update: The nutritional value of each grid is updated to reflect the influence of the surrounding cell population.

[0081] In practical applications, to ensure the biological relevance of the cellular automata model, the model parameters are estimated based on literature values ​​and empirical data related to tumor cell growth and treatment. Specifically, the estimation process consists of the following three stages: 1. Baseline estimation: The data used are cell population data under conditions without treatment of immune cells and macrophages; 2. Immune specificity estimation, controlling immune cells and macrophages based on known outcomes under no-treatment conditions; 3. Tumor-specific estimation: Fitting data using tumor growth and treatment under different treatment strategies and nutritional conditions.

[0082] The solution in this application also provides simulation results of the evolution of tumor cells under different preset conditions, as shown below.

[0083] (1) In the first embodiment, the preset conditions were no treatment, normal nutrition supply, the cell size of the cellular automata model was 100*100, the simulation time was 3 months, and the results were as follows: Figure 12 As shown, Figures (a), (b), and (c) show the evolution of tumor cells without the influence of immune cells, while Figures (d), (e), and (f) show the evolution of tumor cells with the influence of immune cells. Furthermore, Figures (a) and (d) show the change in cell number over time, Figures (b) and (e) show the final spatial distribution of various cell types, and Figures (c) and (f) show the final spatial distribution of nutrients.

[0084] (2) In the second embodiment, the preset conditions were that immune cells were involved, and there was normal supply of treatment and nutrition. The cell size of the cellular automata model was 100*100, and the simulation time was 3 months. The results were as follows: Figure 13 As shown in the figures, Figure (a) illustrates the survival rate and recurrence of tumor cells under targeted therapy, Figure (b) illustrates the survival rate and recurrence of tumor cells under immunotherapy, Figure (c) illustrates the survival rate and recurrence of tumor cells under combined therapy, Figure (d) illustrates the change of drug resistance indicators of tumor cells over time under targeted therapy, Figure (e) illustrates the change of drug resistance indicators of tumor cells over time under immunotherapy, and Figure (f) illustrates the change of drug resistance indicators of tumor cells over time under combined therapy. It can be seen that immunotherapy is slower to take effect than targeted therapy, and its effect is worse than that of targeted therapy. The drug resistance of tumor cells is also weaker.

[0085] Figure 14 The remaining proportion of tumor cells under different treatment methods provided in the embodiments of the present invention, such as... Figure 14 As shown, tumor cells initially decreased with different treatment methods, but eventually recurred. Among them, combination therapy had the best effect on killing and inhibiting tumor cells, and the number of recurring tumor cells was the fewest.

[0086] (3) In the third embodiment, the effects on immune cells and tumor cells were studied under conditions of adequate nutrition, wherein... Figure 15 Figures (a), (b), and (c) show the nutrient reorganization conditions, while figures (d), (e), and (f) show the nutrient deficiency conditions. Figures (a) and (d) show the change in average nutrient levels over time, figures (b) and (e) show the change in the proportion of tumor cells over time, and figures (c) and (f) show the spatial distribution of nutrients. Specifically, the nutrient-sufficient condition refers to the supplementation of 2 mol of nutrients every 6 hours, while the nutrient-deficient condition refers to the supplementation of 0.6 mol of nutrients every 6 hours. It can be seen that when nutrients are sufficient, the number of immune cells increases, which can reduce the number of tumor cells and thus reduce the consumption of nutrients. When nutrients are insufficient, the number of immune cells is low, the number of tumor cells increases, and thus the consumption of nutrients increases.

[0087] (4) In the fourth embodiment, the preset conditions are the same treatment situation, but the nutritional situation is different. Specifically, the initial nutritional situation is the same, but the nutritional value is different after the treatment starts. The conclusion is that adequate nutrition can significantly reduce the number of tumor cells.

[0088] (5) In the fifth embodiment, the preset condition is that under the same treatment conditions, the nutritional value is changed at different times. Specifically, Figure 16 The solid lines represent increased nutrition, and the dashed lines represent decreased nutrition. It can be seen that increasing nutrition can significantly reduce the number of tumor cells, while decreasing nutrition can increase the number of tumor cells.

[0089] Based on the above embodiments, it can be determined that nutrients have a crucial impact on the growth of tumor cells. Specifically, sufficient nutrition can inhibit the growth of tumor cells, while insufficient nutrition will cause tumor cells to grow faster. Therefore, in practical applications, controlling nutrients can be used as an auxiliary means in addition to treatment.

[0090] The modeling and analysis system for tumor cell evolution provided by this invention will be described below. The modeling and analysis system for tumor cell evolution described below can be referred to in correspondence with the modeling and analysis method for tumor cell evolution described above.

[0091] Figure 17 This is a schematic diagram of the structure of the tumor cell evolution modeling and analysis system provided in the embodiments of the present invention.

[0092] like Figure 17 As shown, the modeling and analysis system for tumor cell evolution includes: The model building module 1701 is used to build a cellular automaton model, wherein the cellular automaton model is a two-dimensional structure. The model initialization module 1702 is used to set up an immune microenvironment in the cellular automata model, wherein the immune microenvironment includes immune T cells and macrophages; The cell evolution simulation module 1703 is used to simulate and predict the evolution of tumor cells in the immune microenvironment under preset conditions through the cellular automata module, and to record the evolution process of tumor cells in the cellular automata model.

[0093] The specific implementation method of the tumor cell evolution modeling and analysis system provided in this embodiment can be implemented with reference to the above embodiment, and will not be repeated here.

[0094] Figure 18 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 18 As shown, the electronic device may include: a processor 1810, a communications interface 1820, a memory 1830, and a communication bus 1840, wherein the processor 1810, the communications interface 1820, and the memory 1830 communicate with each other via the communication bus 1840. The processor 1810 can call logical instructions in the memory 1830 to execute a modeling and analysis method for tumor cell evolution, the method including: Construct a cellular automaton model, wherein the cellular automaton model is a two-dimensional structure; An immune microenvironment is set up in the cellular automata model, which includes immune T cells, macrophages, and nutritional conditions; Under preset conditions, the evolution of tumor cells in the immune microenvironment is simulated and predicted by the cellular automata module, and the evolution process of tumor cells in the cellular automata model is recorded.

[0095] Furthermore, the logical instructions in the aforementioned memory 1830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to perform the modeling and analysis methods for tumor cell evolution provided by the above methods, the method including: Construct a cellular automaton model, wherein the cellular automaton model is a two-dimensional structure; An immune microenvironment is set up in the cellular automata model, which includes immune T cells, macrophages, and nutritional conditions; Under preset conditions, the evolution of tumor cells in the immune microenvironment is simulated and predicted by the cellular automata module, and the evolution process of tumor cells in the cellular automata model is recorded.

[0097] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a modeling and analysis method for tumor cell evolution provided by the methods described above, the method comprising: Construct a cellular automaton model, wherein the cellular automaton model is a two-dimensional structure; An immune microenvironment is set up in the cellular automata model, which includes immune T cells, macrophages, and nutritional conditions; Under preset conditions, the evolution of tumor cells in the immune microenvironment is simulated and predicted by the cellular automata module, and the evolution process of tumor cells in the cellular automata model is recorded.

[0098] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0099] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A modeling and analysis method for tumor cell evolution, characterized in that, include: Construct a cellular automaton model, wherein the cellular automaton model is a two-dimensional structure; An immune microenvironment is set up in the cellular automata model, which includes immune T cells, macrophages, and nutritional conditions; Under preset conditions, the evolution of tumor cells in the immune microenvironment is simulated and predicted by the cellular automata module, and the evolution process of tumor cells in the cellular automata model is recorded.

2. The modeling and analysis method for tumor cell evolution according to claim 1, characterized in that, The immune T cells include cytotoxic T cells, regulatory T cells, and helper T cells; The macrophages include M1 macrophages and M2 macrophages.

3. The modeling and analysis method for tumor cell evolution according to claim 1, characterized in that, The process of simulating and predicting the evolution of tumor cells in the immune microenvironment using the cellular automata module under preset conditions includes: The tumor cells are placed at the center of the cellular automata model; The immune T cells and the macrophages are deployed to the periphery of the cellular automata model.

4. The modeling and analysis method for tumor cell evolution according to claim 2, characterized in that, Setting up an immune microenvironment in the cellular automata model includes: The differentiation rate of tumor cells in the cellular automata model is set. The cellular heterogeneity of tumor cells and cytotoxic T cells in the cellular automata model is configured. The proliferation probability, death probability, and migration probability of tumor cells, immune T cells, and macrophages in the cellular automata model are set.

5. The modeling and analysis method for tumor cell evolution according to claim 2, characterized in that, Setting up an immune microenvironment in the cellular automata model further includes: The nutritional conditions in the cellular automata model are set.

6. The modeling and analysis method for tumor cell evolution according to claim 1, characterized in that, The preset conditions include factors that influence the evolution of tumor cells; The process of simulating and predicting the evolution of tumor cells in the immune microenvironment using the cellular automata module under preset conditions includes: A control experiment was set up based on the aforementioned influencing factors. The evolution process of tumor cells was simulated using the cellular automata model, and the evolution process was recorded.

7. The modeling and analysis method for tumor cell evolution according to claim 6, characterized in that, The influencing factors include treatment methods, treatment duration, nutritional supply, and the presence or absence of immune cell intervention.

8. A modeling and analysis system for tumor cell evolution, employing the modeling and analysis method for tumor cell evolution as described in any one of claims 1-7, characterized in that, include: The model building module is used to build a cellular automaton model, which is a two-dimensional structure. A model initialization module is used to set up an immune microenvironment in the cellular automata model, wherein the immune microenvironment includes immune T cells and macrophages; The cell evolution simulation module is used to simulate and predict the evolution of tumor cells in the immune microenvironment under preset conditions through the cellular automata module, and to record the evolution process of tumor cells in the cellular automata model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the modeling and analysis method for tumor cell evolution as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing a computer program, characterized in that, When a computer program is executed by a processor, it implements the modeling and analysis method for tumor cell evolution as described in any one of claims 1-7.

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