Malignant tumor treatment simulation system and method considering immune system

Abstract

The invention discloses a malignant tumor treatment simulation system and method considering an immune system, and relates to the field of computational biology and medical informatics. Based on the conceptual model, constructing a mathematical model of interaction between the immune system and the malignant tumor; wherein basic dynamics of cells and factors is reduced by using an ordinary differential equation, and interaction is added on the basis; simulating and verifying the mathematical model based on the biological data to obtain an accurate mathematical model; and performing malignant tumor immunotherapy simulation based on the accurate mathematical model to obtain an immunotherapy simulation effect. By adding an immune system and an escape mechanism, the physiological process of malignant tumor invasion is perfected, an analysis tool is provided for understanding the effect of the immune system in malignant tumor progression, and a theoretical basis is also laid for optimizing an immunotherapy strategy.

Description

Technical Field

[0001] This invention belongs to the fields of computational biology and medical informatics, specifically relating to a system and method for simulating the treatment of malignant tumors that takes into account the immune system. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Cancer is a major global disease that seriously threatens human life and health. Based on its biological behavior, cancer can be divided into benign and malignant tumors. Benign tumors grow locally and can usually be cured by surgical removal; while malignant tumors are characterized by invasive growth, high recurrence rate, and distant metastasis, often requiring multidisciplinary comprehensive treatment, and even then, the efficacy is often limited. Furthermore, malignant tumor cells can spread to other parts of the body through the bloodstream and lymphatic system, forming metastatic lesions, which is one of the main causes of treatment failure and patient death. Currently, the prevention and treatment of malignant tumors remains a major challenge facing the global medical community.

[0004] Multiple myeloma, a typical malignant hematologic malignancy, is characterized by the clonal proliferation of malignant plasma cells in the bone marrow, often leading to multiple organ dysfunction. In recent years, with the application of targeted drugs such as monoclonal antibodies and proteasome inhibitors, as well as stem cell transplantation technology, the median survival of patients has improved. However, the disease remains difficult to cure, and most patients face repeated relapses, ultimately dying from disease progression or treatment-related complications.

[0005] The immune system plays a crucial role in the prevention, treatment, and recurrence of tumors. In healthy individuals, the immune system recognizes and eliminates malignant cells. Tumor antigens are taken up by antigen-presenting cells and presented to T cells, prompting T cell activation and differentiation into effector T cells, which then migrate to the tumor site and kill tumor cells. However, tumor cells can evade immune clearance through mechanisms such as immune editing; this process is called immune escape. Immune escape is a vital mechanism for the survival of malignant tumors, allowing tumor cells to evade immune surveillance and thus progress the disease.

[0006] Chimeric antigen receptor T-cell (CAR-T) therapy is a special type of immunotherapy that kills tumor cells by targeting specific antigens on tumor cells. It has been approved for the treatment of various tumors, including multiple myeloma. However, CAR-T therapy still faces challenges, such as the treatment of related complications and the relapse of some patients (especially after anti-BCMA CAR-T therapy). There is an urgent need to explore new targets.

[0007] Mathematical modeling can simulate physiological changes in cells or cytokines using ordinary differential equations (ODEs). Specifically, ODEs are used to construct a mathematical model of a specific cell-related physiological activity by simulating physiological activities between cells, between cells and factors, and between factors. However, current mathematical modeling of tumors and the immune system suffers from gaps, resulting in an inability to accurately simulate the physiological processes of malignant tumors in the human body. On one hand, most mathematical models of malignant tumors fail to incorporate tumor heterogeneity, a core characteristic of tumors, into their modeling framework. In fact, tumor heterogeneity plays a decisive role in the occurrence, development, and invasion of tumors, and its dynamic interactions with immune cells constitute a complex regulatory network. Ignoring this crucial factor can lead to biases in our understanding of the pathogenesis of malignant tumors. Even when some models consider tumor heterogeneity, they do not delve into the interactions between different tumor subtypes. On the other hand, some models focus on constructing mathematical models at the immune cell level to analyze immune regulatory mechanisms and their dynamic characteristics under physiological / pathological conditions. However, these models often lack the organic integration of immune factor mechanisms and fail to fully consider the impact of tumors on the immune system through immune factors, thus making it difficult to accurately simulate the actual pathogenesis of malignant tumors. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, this invention provides a malignant tumor treatment simulation system and method that considers the immune system. Specifically, it is a system and its construction method for simulating malignant tumor growth, immune escape, and treatment response. The system introduces immune mechanisms into the dynamic simulation of tumor growth and invasion by constructing a mathematical model of malignant tumors that includes immune components. Specifically, the model incorporates an immune escape mechanism based on tumor heterogeneity. By establishing different competing tumor subtypes and simulating the immunosuppressive microenvironment created by immune cells and immune factors, the invasion process of malignant tumors in vivo is more realistically reproduced. Furthermore, the system integrates a chimeric antigen receptor T-cell therapy module, which can simulate the dynamic intervention effect of this immunotherapy method on the tumor-immune system. This invention provides an analytical tool for understanding the role of the immune system in the progression of malignant tumors and lays a theoretical foundation for optimizing immunotherapy strategies.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In a first aspect, the present invention provides a malignant tumor treatment simulation system that takes into account the immune system, comprising: The conceptual model building module is used to construct conceptual models of the interaction between the immune system and malignant tumors. The mathematical model building module is used to construct a mathematical model of the interaction between the immune system and malignant tumors based on a conceptual model. It first uses ordinary differential equations to reconstruct the basic dynamics of cells and factors, and then adds the interaction on this basis. The model validation module is used to simulate and validate mathematical models based on biological data to obtain accurate mathematical models. The model simulation module is used to simulate immunotherapy for malignant tumors based on accurate mathematical models, and to obtain the simulated effects of immunotherapy.

[0010] A further technical solution involves constructing a conceptual model of the interaction between the immune system and malignant tumors. Specifically, this involves selecting immune cells and immune factors related to malignant tumors, then adding tumor immune evasion mechanisms to construct a conceptual network of the interaction between the immune system and malignant tumors.

[0011] In a further technical solution, the immune cells include natural killer cells, cytotoxic T lymphocytes, and regulatory T cells, and the immune factors include interleukin-2 and interleukin-10.

[0012] A further technical solution, specifically constructing a mathematical model of the interaction between the immune system and malignant tumors, is as follows: For immune cells and immune factors, based on conceptual models, the physiological processes involved in their generation, apoptosis, or consumption are identified, and they are mathematically characterized using ordinary differential equations. In the tumor microenvironment model, interactions between cells, between cells and factors, and between factors are introduced; We introduce ordinary differential equations characterizing malignant tumors into the normal model and construct a tumor antigen escape model.

[0013] A further technical solution involves quantifying the interaction using a Hill function and integrating it as an additional term into the corresponding ordinary differential equation to construct a normal physiological homeostasis model.

[0014] A further technical solution for constructing a malignant tumor antigen escape model is as follows: Malignant tumors are classified into four subtypes based on their ability to evade immune recognition. The model incorporates a total tumor count constraint. By summing up the differential equations corresponding to all subtypes, a differential equation describing the total number of malignant tumor cells is constructed.

[0015] A further technical solution involves introducing exogenous chimeric antigen receptor T cells into the immunotherapy simulation of malignant tumors.

[0016] Secondly, the present invention provides a method for simulating malignant tumor treatment that takes into account the immune system, comprising: Constructing a conceptual model of the interaction between the immune system and malignant tumors; Based on a conceptual model, a mathematical model of the interaction between the immune system and malignant tumors is constructed. In this model, the basic dynamics of cells and factors are first reduced using ordinary differential equations, and then the interaction is added. The mathematical model is simulated and verified based on biological data to obtain an accurate mathematical model; Immunotherapy for malignant tumors was simulated using an accurate mathematical model, and the simulated effects of immunotherapy were obtained.

[0017] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for simulating the treatment of malignant tumors taking into account the immune system as described in the second aspect.

[0018] Fourthly, the present invention provides a computer 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 the steps in the method for simulating the treatment of malignant tumors taking into account the immune system as described in the second aspect.

[0019] The above one or more technical solutions have the following beneficial effects: This invention constructs a mathematical model of malignant tumors that includes immune escape mechanisms and immunotherapy interventions. This model, by screening key immune cells and immune factors, introduces the immune escape mechanism of malignant tumors and constructs a conceptual network of the interaction between the immune system and malignant tumors. Based on this, a mathematical model describing this interaction is established. Differential equations are used to transform the conceptual model into mathematical expressions. First, ordinary differential equations characterize the basic dynamic processes of cells and factors (including cell generation and apoptosis). Then, various interactions are introduced to refine the dynamic behavior of cells and factors in the in vivo physiological environment. The model is validated and its parameters optimized using existing experimental or clinical data to ensure its reliability. Furthermore, an exogenous chimeric antigen receptor T-cell (CAR-T) therapy module is integrated. Based on the constructed immune system model, dynamic simulation and evaluation of the immunotherapy effect are achieved.

[0020] This invention is the first to organically integrate the immune system with the immune escape mechanism of malignant tumors. By introducing immune system components to realistically recreate the dynamic behavior during tumor invasion, it significantly reduces the deviation between model simulation results and real biological data. Using this model, the dynamic changes in the number of tumor cells during malignant tumor invasion, as well as the dynamic response of the immune system under tumor stress, can be accurately simulated.

[0021] Based on the tumor competition mechanism and the escape characteristics of immune cell recognition ability, this invention classifies malignant tumors into four subtypes. These four tumor subtypes compete with each other in a resource-limited environment, thereby effectively reflecting the evolution of the population size of each tumor subtype under immune pressure selection.

[0022] Based on the close interaction between chimeric antigen receptor T cells (CAR-T) and the immune system in the bone microenvironment, this invention achieves mathematical modeling of the CAR-T treatment process, providing an effective tool for the quantitative evaluation of immunotherapy strategies. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 This is a flowchart of a method for simulating the treatment of malignant tumors considering the immune system, as described in an embodiment of the present invention. Figure 2 This is a flowchart of a multiple myeloma treatment simulation method considering the immune system, as described in an embodiment of the present invention. Figure 3 This is a conceptual model diagram of a mathematical model of multiple myeloma in malignant tumors with immune escape mechanisms and immunotherapy, as described in this embodiment of the invention. Detailed Implementation

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0027] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] In particular, this invention can be applied to malignant tumors with complex immune microenvironments, such as multiple myeloma, breast cancer, and liver cancer. The following will use multiple myeloma as an example for detailed explanation.

[0029] Example 1 like Figure 1 As shown, this embodiment discloses a malignant tumor treatment simulation system that takes into account the immune system, including a conceptual model building module, a mathematical model building module, a model validation module, and a model simulation module.

[0030] The overall inventive concept of this invention is to recreate the real tumor invasion process by incorporating the immune system. Its core idea is to complete the depiction of the malignant tumor invasion process by understanding the interaction between the immune system and malignant tumors (such as multiple myeloma). Figure 2 As shown, firstly, the composition of the immune system is investigated. The immune system functions through various cells and factors, including antigen-presenting cells, Th1 and Th2 cells, while natural killer cells and cytotoxic T lymphocytes can directly kill tumor cells. To illustrate the interaction between immunity and tumors, natural killer cells and cytotoxic T lymphocytes were selected. Subsequently, to demonstrate the regulatory role of the immune system, such as... Figure 3 As shown, regulatory T cells, interleukin-2, and interleukin-10 were selected, and a tumor escape mechanism was added to further refine the mechanism of action and form a conceptual model. Next, a mathematical model of the interaction between the immune system and multiple myeloma was constructed, and the conceptual model was transformed into mathematical expressions using differential equations. Then, the model was optimized and validated using existing data, and the model parameters were adjusted based on the simulation results. Finally, chimeric antigen receptor T cell therapy was added. The entire framework, by incorporating the immune system, recreates the invasion process of multiple myeloma.

[0031] The entire modeling framework, by introducing immune system components and their interaction with tumors, achieves a more comprehensive and realistic dynamic reconstruction of the invasion process of malignant tumors (such as multiple myeloma).

[0032] The conceptual model building module is used to construct a conceptual model of the interaction between the immune system and malignant tumors. It selects immune cells and immune factors related to tumors, and then adds tumor immune evasion mechanisms to construct a conceptual network of the interaction between the immune system and malignant tumors.

[0033] In this embodiment, the main mechanisms by which the immune system fights tumors are clarified, and the functions of key cells and factors are identified, specifically including: Malignant tumors will be illustrated using multiple myeloma as an example.

[0034] S101: Investigate the composition of the immune system and its role in tumor immunity, selecting immune system components related to multiple myeloma.

[0035] The immune system is closely related to the occurrence, development, and prognosis of tumors. It mainly includes immune organs, immune cells, and immune factors, and can be divided into specific immunity and non-specific immunity based on response characteristics. The immune system contains various immune cells and immune factors. Some cells are closely related to the invasion process of multiple myeloma. Cells and factors closely related to the immune process of multiple myeloma have been screened, including natural killer cells (NK cells), cytotoxic T lymphocytes (CTLs), and regulatory T cells (Tregs), as well as interleukin-2 (IL-2) and interleukin-10 (IL-10). Natural killer cells (NK cells) and cytotoxic T lymphocytes (CTLs) can directly kill cancer cells, regulatory T cells (Tregs) can inhibit the function of immune cells by secreting interleukin-10, while interleukin-2 can promote the function of immune cells.

[0036] Representative cells were selected from specific and non-specific immunity: natural killer cells, as key effector cells of non-specific immunity, are the earliest activated components of innate immunity; cytotoxic T lymphocytes are the core cells of specific immunity. Both can kill target cells through degranulation and the Fas-FasL pathway.

[0037] Select regulatory cells with immunosuppressive function: Regulatory T cells can suppress excessive immune responses and prevent autoimmune damage, and their high-level expression is associated with poor prognosis in patients with multiple myeloma.

[0038] Screening for key immunomodulatory factors: Interleukin-2 is mainly composed of CD4+ + T cell production can promote T cell proliferation, regulatory T cell differentiation and enhance NK cell activity; interleukin-10 is an important anti-inflammatory factor, and in this invention only the portion secreted by regulatory T cells is considered.

[0039] Simplify the roles of other immune components, construct a conceptual model of the immune system, and form a conceptual model diagram, such as... Figure 3 As shown, natural killer cells and cytotoxic T lymphocytes represent immune killing function, regulatory T cells represent immunosuppressive mechanisms, and interleukin-2 and interleukin-10 represent immune regulatory factors, thereby establishing a simplified conceptual model of the immune system.

[0040] S102: Investigate the immune escape mechanism of multiple myeloma, construct two immune mechanisms by which multiple myeloma evades elimination by the immune system, and analyze the interaction between tumor and immune system.

[0041] Multiple myeloma can evade immune surveillance through mechanisms such as antigen escape and immunosuppression. It can also suppress the killing of immune cells by promoting the function of regulatory T cells and secreting interleukin-10, and evade the recognition of immune cells through immune editing, including inhibiting T cell activation and proliferation, disrupting antigen presentation, and inducing T cell exhaustion.

[0042] The selected immune cells / immunomodulatory factors were integrated with the tumor immune escape mechanism. Specifically, antigen escape was modeled through four tumor subtypes: Subtype 1: Cannot escape recognition by both types of immune cells and can be killed by NK cells and CTLs; Subtype 2: Can escape NK cell recognition but is still recognized by CTLs; Subtype 3: Can escape CTL recognition but is still recognized by NK cells; Subtype 4: Can escape recognition by both types of immune cells simultaneously.

[0043] The immunosuppressive mechanism is manifested through the promoting effect of multiple myeloma cells on regulatory T cells and interleukin-10.

[0044] S103: The mathematical model building module is used to construct a conceptual model of the interaction between the immune system and multiple myeloma. Based on the selected immune cells and immune factors, and according to the physiological activities in the human body, the corresponding cell-factor interaction network is constructed by simplifying and omitting parts that are not related to the main research object.

[0045] The corresponding cell-factor interaction network was developed by reviewing a large amount of medical literature, screening for the physiological activities of corresponding cells, and ultimately forming a simplified physiological activity network, such as... Figure 3 A simplified diagram of physiological activities.

[0046] The mathematical model building module is used to construct a mathematical model of the interaction between the immune system and malignant tumors based on the conceptual model. It uses differential equations to transform the conceptual model into mathematical expressions. First, it uses ordinary differential equations to restore the basic dynamics of cells and factors, namely the processes of generation and apoptosis. On this basis, the interaction is added to improve the physiology of cells and factors in vivo.

[0047] In this embodiment, taking multiple myeloma as an example, a conceptual model of the interaction between the immune system and multiple myeloma is analyzed. For each selected immune cell and regulatory factor, a basic dynamic differential equation is established based on its biological origin and destination.

[0048] Biological origin, for cells, refers to physiological activities such as differentiation or proliferation of myeloid cells that increase the number of corresponding cells; for factors, it refers to activities such as cell secretion. Destination refers to physiological activities such as apoptosis or aging of corresponding cells that decrease the number of cells; for factors, it refers to activities such as consumption and decomposition. These form the basis for constructing biological mathematical models.

[0049] Biological processes are complex, with multiple factors having either promoting or inhibiting effects on the same physiological activity. Here, "basic" refers to the most important physiological activity that determines the change in oxidant concentration without the influence of other cells or factors in the model. Factors not considered in the model or still unknown in current research constitute the coefficients of the equation in the form of constants.

[0050] S201: For selected immune cells and immune factors, based on a conceptual model, the basic physiological processes such as the generation and apoptosis (or consumption) of each cell and factor are determined (without adding the main physiological activities that determine the concentration of the corresponding substance, which determine the effects of other cells and factors); subsequently, the basic physiological processes are mathematically characterized in the form of ordinary differential equations. The left side of the differential equation is the rate of change of the substance concentration over time (i.e., the first derivative d[X] / dt), and the right side is a mathematical expression that comprehensively describes the contribution of the physiological processes to the concentration change.

[0051] S202: Changes in factor concentration over time are primarily driven by physiological processes such as cell generation, apoptosis, or consumption. Specifically, cell generation originates from mechanisms such as myeloid cell differentiation and cell division; apoptosis is mediated by autonomous cell death and the clearance action of other cells; factor generation is mainly achieved through cellular secretion, while consumption stems from their inactivation after binding to their corresponding receptors. To quantify these processes, they are characterized by multiplying coefficients by concentrations, serving as the basis for constructing differential equations.

[0052] S203: In the basic mathematical model constructed above, interactions between cells, between cells and factors, and between factors are introduced. These interactions are manifested as the promoting or inhibiting effects of specific cells or factors on the aforementioned physiological processes (such as generation, apoptosis, or consumption). This effect is quantified using the Hill function and integrated as an additional term into the corresponding ordinary differential equations, thereby constructing a more complete model of normal physiological homeostasis.

[0053] Based on the established interaction relationships in the conceptual model, the fundamental differential equations are refined. Specifically, mathematical terms characterizing the interactions are introduced into the fundamental differential equations. To quantify the interactions, this invention uses the Hill function to describe them, and its general form can be expressed as the following equation: (1) (2) in, Indicates ligand concentration. This indicates the maximum expression level of the promoter. It is a regulation function The coefficient of steepness, Indicates a promoting effect. Indicates an inhibitory effect. and Let represent the dissociation constants, respectively. The Shell function is used to represent the dissociation constants. The cellular interactions of ligand-receptor binding are represented by functions, and equations (1) and (2) describe the stimulatory and inhibitory functions of ligand-receptor binding.

[0054] S204: Ordinary differential equations characterizing multiple myeloma are introduced into the normal model. Based on the proliferative characteristics of multiple myeloma, a growth function consistent with its growth pattern is selected for mathematical description. Simultaneously, to simulate the immune escape function of multiple myeloma, its immunosuppressive mechanism is integrated into the model: by modeling the process by which multiple myeloma cells promote the proliferation of regulatory T cells and secrete interleukin-10, this is ultimately reflected in the equation as an inhibitory term on the function of immune effector cells. Furthermore, to characterize the antigen escape characteristics of multiple myeloma, its main subtypes are modeled using independent ordinary differential equations to simulate the clonal diversity within the tumor and its differentiated immune interactions.

[0055] The normal model refers to the state model of a person when they are not sick, and does not include the tumor. That is, it does not include tumor-related differential equations and effects. For example, formulas (3) to (7) are normal models that do not include the effects of tumors.

[0056] In this invention, and Assuming all values ​​are 1, the final constructed immune differential equation is as follows: (3) (4)

[0057] (6) (7) in, , , , and These represent the concentrations of natural killer cells, cytotoxic T lymphocytes, interleukin-2, interleukin-10, and regulatory T cells, respectively. , and Indicates the rate of lymphocyte inflow. , and Indicates the rate of apoptosis of immune cells. and These represent the activation functions of antigens on immune cells. , , This indicates the proliferative effect mediated by interleukin-2. , This indicates the pro-apoptotic effect mediated by interleukin-10. , It refers to the secretion rate of interleukin-2 by CD4+ T lymphocytes and cytotoxic T lymphocytes. , and It represents the rate at which cytotoxic T lymphocytes, natural killer cells, and regulatory T cells consume interleukin-2. It refers to the rate of interleukin-10 secretion by regulatory cells. It is the rate of consumption of interleukin-10. , , , , The values ​​represent the inhibitory effects of interleukin-10 on natural killer cells and cytotoxic T lymphocytes, and the promoting effects of interleukin-2 on natural killer cells, cytotoxic T lymphocytes, and regulatory T cells, respectively. CD4 cells are not the primary cells considered in this invention and are therefore set as a fixed value.

[0058] Furthermore, a model of the interaction between multiple myeloma and the immune system is constructed: First, the basic equation for the proliferation process of multiple myeloma is established. Specifically, based on the growth characteristics of multiple myeloma and existing technologies, its proliferation kinetic equation is constructed as follows: (8) in, and These represent the proliferation and apoptosis coefficients of multiple myeloma, respectively. This indicates the concentration in multiple myeloma. This indicates the maximum number of tumor cells the environment can tolerate. and This indicates the promoting effect of IL6 and VCAM1 on tumor proliferation. This indicates that SLRPS has an inhibitory effect on tumor apoptosis. This indicates the promoting effect of JNK_PP factor on apoptosis. IL6, VCAM1, SLRPS, and JNK_PP are all compounds in the bone microenvironment and are closely related to tumor growth.

[0059] Based on the growth characteristics of multiple myeloma and existing technologies, it is known that under sufficient resources, tumors will grow exponentially, but under a fixed environmental carrying capacity, tumor growth is slow in the early and late stages, and rapid in the middle stage. Regarding tumor proliferation dynamics, this invention, referencing existing technologies, proposes new aspects related to tumor heterogeneity and its suppressive effect on the immune system.

[0060] Furthermore, multiple myeloma possesses the ability to evade recognition by immune cells (including natural killer cells and cytotoxic T lymphocytes) due to antigen escape. To simulate this biological characteristic, a multiple myeloma (tumor) antigen escape model is constructed as follows: Multiple myeloma is divided into four subtypes based on its ability to escape immune recognition; considering the limited resources in the bone microenvironment, there is competition for these limited resources among the subtypes. To simulate this competition mechanism, a total tumor cell count constraint is introduced into the model, meaning the total number of cells in the four subtypes is limited by a preset upper limit, representing the sharing and competition mechanism for limited environmental resources; to characterize the dynamic changes in the total number of multiple myeloma cells, the differential equations corresponding to all subtypes are summed to construct a differential equation describing the total number of multiple myeloma cells. The tumor antigen escape model incorporates tumor heterogeneity and the suppressive tumor microenvironment.

[0061] Specifically, an interaction equation between multiple myeloma and the immune system is constructed. This interaction specifically refers to the physiological activities of the immune system eliminating tumors and tumors escaping the immune system. For the tumor equation, the killing effect of the immune system is added to the basic tumor growth and apoptosis rates, and the single tumor growth function is divided into four categories to reflect the heterogeneity in tumor immune escape. For the immune system, the inhibitory effect of multiple myeloma on the immune system is added, such as the secretion of the immunosuppressive factor interleukin-10 and the promotion of the proliferation of immunosuppressive regulatory T cells. Specifically, the antigen escape mechanism of multiple myeloma is first constructed. Compared to a single type of tumor cell, the growth rates of tumor cells divided into four categories will inevitably differ under immune pressure, but the maximum number of tumor cells that the bone microenvironment can support is fixed. To address this issue, this invention proposes a new shared environment maximum carrying capacity to reflect competition among tumor cells, specifically the following equation: (9) Where Ti represents the tumor cells, the left side represents the tumor's derivative with respect to time, i.e., the rate of change in tumor number; a is the growth rate. 'b' represents the total number of tumor cells, and 'b' represents the maximum carrying capacity of the environment.

[0062] Based on the different abilities of tumor cells to evade immune cell recognition, tumor cells are divided into four categories: The first subtype cannot evade recognition by either of the two types of immune cells mentioned above; its differential equation includes killing terms mediated by natural killer cells and cytotoxic T lymphocytes on the right side. The second subtype can evade recognition by natural killer cells but can still be recognized by cytotoxic T lymphocytes; its differential equation only includes killing terms mediated by cytotoxic T lymphocytes on the right side. The third subtype can evade recognition by cytotoxic T lymphocytes but can still be recognized by natural killer cells; its differential equation only includes killing terms mediated by natural killer cells on the right side. The fourth subtype can evade recognition by both types of immune cells simultaneously; its differential equation does not contain any immune killing terms on the right side. The following differential equation is constructed:

[0063]

[0064]

[0065]

[0066] (14) in, , , and These represent the concentrations for the four subtypes of multiple myeloma. , , and These represent the killing rates of natural killer cells and cytotoxic T lymphocytes on the corresponding cells, respectively. This indicates that interleukin-10 inhibits the corresponding immune response.

[0067] Furthermore, to construct the immunosuppressive mechanism of multiple myeloma, and to demonstrate the ability of multiple myeloma to create a suppressive immune microenvironment, this invention incorporates the role of multiple myeloma into the differential equations of regulatory T cells and interleukin-10. The newly proposed differential equations are as follows: (15) (16) in, This refers to the additional rate of interleukin-10 secretion in newly added multiple myeloma. This is due to the proliferative effect of contact on regulatory T cells in multiple myeloma. It is the reaction rate.

[0068] The model validation module is used to simulate and validate mathematical models based on biological data to obtain accurate mathematical models. In this embodiment, multiple myeloma is used as an example to verify and optimize the effectiveness of the mathematical model using existing data. The constructed mathematical model is used to simulate the dynamic invasion process of multiple myeloma in the bone microenvironment; the simulated data is compared with existing experimental or clinical observation data on tumor invasion; based on the differences between the model output and the actual data, a parameter estimation algorithm is used to iteratively optimize specific parameters in the model to improve its accuracy and predictive ability.

[0069] Initial parameters of the model are set, and the system of differential equations is solved and simulated using a numerical integrator for ordinary differential equations. In practice, initial parameter values ​​are first estimated based on existing biological data. The constructed differential equation system is then input into the MATLAB simulation environment, and numerical simulation is performed using the ode45 solver. The simulation output is analyzed and compared with experimental or clinical data. If significant deviations are found, optimization algorithms such as genetic algorithms are used to iteratively calibrate the model parameters to improve model accuracy and predictive ability.

[0070] The model simulation module is used to simulate immunotherapy for malignant tumors based on accurate mathematical models, obtaining simulated immunotherapy effects. During immunotherapy simulation, exogenous chimeric antigen receptor T cell therapy is incorporated, utilizing a well-established immune system model to simulate the effects of immunotherapy.

[0071] In this embodiment, taking multiple myeloma as an example, the mechanism of action of chimeric antigen receptor T cells (CAR-T cells) is analyzed: CAR-T cells are an adoptive immunotherapy method, introduced into the body via in vitro infusion, and can persist in the body for a certain period of time, specifically recognizing and killing tumor cells expressing corresponding antigens. After infusion, CAR-T cells migrate to the lesion site, differentiate into memory CAR-T cells in the bone microenvironment, and are regulated by local immune factors (such as interleukins). Based on their distribution and functional status, CAR-T cells are divided into three categories: the first category is the initial CAR-T cells after infusion; the second category is effector CAR-T cells that migrate to the bone microenvironment, whose functional activity is regulated by the concentrations of interleukin-2 and interleukin-10 in the bone microenvironment; and the third category is memory CAR-T cells, which have the ability to self-proliferate and differentiate into effector cells.

[0072] Based on the above mechanisms of action, differential equations were constructed for three types of CAR-T cells. The concentration of the first type of CAR-T cells is mainly determined by the infusion dose and the in vivo metabolic rate. The second type of CAR-T cells originates from the migration of the first type of cells into the bone microenvironment, and their destination includes apoptosis and differentiation into memory cells. The concentration of the third type of memory CAR-T cells is influenced by both self-proliferation and transformation from the second type of cells. By analyzing the origin (e.g., infusion, migration, differentiation) and apoptosis (e.g., apoptosis, inactivation) processes of each type of cell, the preliminary form of their kinetic equations is established as follows: (17)

[0073] (19) in, , and These are the concentrations of infused, effector, and memory chimeric antigen receptor T cells, respectively. It is the apoptosis rate of the infused chimeric antigen receptor T cells. It is the rate at which chimeric antigen receptor T cells enter the microenvironment. It is the proliferation function of effector chimeric antigen receptor T cells. It is the rate at which memory cells transform into effector cells. It is the apoptosis rate of effector chimeric antigen receptor T cells. It is the rate at which it is converted into memory cells. and The effects of interleukin-2 and interleukin-10 on the promotion and inhibition of effector chimeric antigen receptor T cells are discussed. It is the apoptosis rate of memory antigen receptor T cells. , , The effects of multiple myeloma antigen on chimeric antigen receptor T cells (CMRT), interleukin-2 on CMRT, and interleukin-10 on CMRT are represented, respectively. Finally, simulations were performed using MATLAB's ode45 integrator.

[0074] It should be noted that the proliferation and function of the second type of CAR-T cells located in the bone microenvironment are regulated by this local immunosuppressive microenvironment. Therefore, the inhibitory factors (such as interleukin-10) should be introduced into the corresponding differential equation to modulate their proliferation rate.

[0075] In summary, this invention provides a mathematical model of malignant tumors with immune escape mechanisms and immunotherapy functions, dynamically simulating the invasion process of malignant tumors by introducing immune system components. Specifically, a simplified immune system framework is constructed using three types of immune cells and two immune factors: natural killer cells and cytotoxic T lymphocytes represent the killing effect of the immune system on tumor cells; regulatory T cells and interleukin-10 represent the negative regulatory mechanism of the immune system; and interleukin-2 represents the positive regulatory effect of the immune system, thereby achieving the abstraction and integration of key functions of the immune system. To further simulate tumor immune escape behavior, this invention also introduces two immune escape mechanisms: by classifying malignant tumors into different subtypes to simulate their heterogeneity, and combining their promoting effect on the immunosuppressive microenvironment, the process of tumor cells escaping immune surveillance and achieving disease progression is reproduced. Finally, based on the constructed immune system-tumor interaction model, a chimeric antigen receptor T cell therapy module is integrated for quantitatively evaluating the effectiveness of immunotherapy strategies.

[0076] Example 2 This embodiment discloses a method for simulating malignant tumor treatment that takes into account the immune system, including: Constructing a conceptual model of the interaction between the immune system and malignant tumors; Based on a conceptual model, a mathematical model of the interaction between the immune system and malignant tumors is constructed. In this model, the basic dynamics of cells and factors are first reduced using ordinary differential equations, and then the interaction is added. The mathematical model is simulated and verified based on biological data to obtain an accurate mathematical model; Immunotherapy for malignant tumors was simulated using an accurate mathematical model, and the simulated effects of immunotherapy were obtained.

[0077] Example 3 The purpose of this embodiment is to provide a computing 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 the steps of the method of Embodiment 2.

[0078] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method of Embodiment 2.

[0079] The steps and methods involved in the apparatuses of Embodiments 3 and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0080] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0082] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A malignant tumor treatment simulation system considering the immune system, characterized in that, include: The conceptual model building module is used to construct conceptual models of the interaction between the immune system and malignant tumors. The mathematical model building module is used to construct a mathematical model of the interaction between the immune system and malignant tumors based on a conceptual model. It first uses ordinary differential equations to reconstruct the basic dynamics of cells and factors, and then adds the interaction on this basis. The model validation module is used to simulate and validate mathematical models based on biological data to obtain accurate mathematical models. The model simulation module is used to simulate immunotherapy for malignant tumors based on accurate mathematical models, and to obtain the simulated effects of immunotherapy.

2. The malignant tumor treatment simulation system considering the immune system as described in claim 1, characterized in that, The conceptual model of the interaction between the immune system and malignant tumors is constructed by selecting immune cells and immune factors related to malignant tumors, and then adding tumor immune evasion mechanisms to construct a conceptual network of the interaction between the immune system and malignant tumors.

3. The malignant tumor treatment simulation system considering the immune system as described in claim 2, characterized in that, The immune cells include natural killer cells, cytotoxic T lymphocytes, and regulatory T cells, and the immune factors include interleukin-2 and interleukin-10.

4. The malignant tumor treatment simulation system considering the immune system as described in claim 1, characterized in that, The mathematical model for the interaction between the immune system and malignant tumors is constructed as follows: For immune cells and immune factors, based on conceptual models, the physiological processes involved in their generation, apoptosis, or consumption are identified, and they are mathematically characterized using ordinary differential equations. In the tumor microenvironment model, interactions between cells, between cells and factors, and between factors are introduced; We introduce ordinary differential equations characterizing malignant tumors into the normal model and construct a tumor antigen escape model.

5. The malignant tumor treatment simulation system considering the immune system as described in claim 4, characterized in that, The interaction is quantified by the Hill function and integrated as an additional term into the corresponding ordinary differential equation to construct a normal physiological homeostasis model.

6. The malignant tumor treatment simulation system considering the immune system as described in claim 4, characterized in that, The specific steps for constructing a malignant tumor antigen escape model are as follows: Malignant tumors are classified into four subtypes based on their ability to evade immune recognition. The model incorporates a total tumor count constraint. By summing up the differential equations corresponding to all subtypes, a differential equation describing the total number of malignant tumor cells is constructed.

7. The malignant tumor treatment simulation system considering the immune system as described in claim 1, characterized in that, Introducing exogenous chimeric antigen receptor T cell therapy when performing immunotherapy simulations for malignant tumors.

8. A method for simulating the treatment of malignant tumors by considering the immune system, characterized in that, include: Constructing a conceptual model of the interaction between the immune system and malignant tumors; Based on a conceptual model, a mathematical model of the interaction between the immune system and malignant tumors is constructed. In this model, the basic dynamics of cells and factors are first reduced using ordinary differential equations, and then the interaction is added. The mathematical model is simulated and verified based on biological data to obtain an accurate mathematical model; Immunotherapy for malignant tumors was simulated using an accurate mathematical model, and the simulated effects of immunotherapy were obtained.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps in the method for simulating the treatment of malignant tumors that takes into account the immune system as described in claim 8.

10. A computer 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 steps in the malignant tumor treatment simulation method considering the immune system as described in claim 8.

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