Computer-implemented methods and apparatus for tumor therapy strategy prediction

Abstract

The application provides a computer-implemented method and device for tumor treatment strategy prediction, and belongs to the technical field of biological medicine. The method comprises the following steps: obtaining the proportion of target antigen-positive immune cells in total immune cells in a target sample; based on the difference between the proportion and a predetermined threshold, dividing the antigen expression level of the target sample to obtain a grouping result; wherein the grouping result is any one of N preset level groupings, and N is an integer not less than 1; based on the grouping result, matching a corresponding treatment strategy, wherein the treatment strategy comprises: administering an effective dose of an antibody corresponding to the target antigen and / or an immune checkpoint inhibitor antibody. By constructing a multi-level hierarchical mechanism based on the expression proportion of immune-related antigens and establishing a mapping relationship between the hierarchical result and the treatment strategy, fine immunotyping of tumor patients and individualized treatment strategy prediction are realized, so that the accuracy, safety and medical resource utilization rate of immunotherapy are improved.

Description

Technical Field

[0001] This application belongs to the field of biomedical technology. Specifically, this application relates to a computer implementation method for predicting tumor treatment strategies, a computer implementation apparatus for predicting tumor treatment strategies, a computing device, and a computer-readable storage medium. Background Technology

[0002] With the development of tumor immunotherapy, especially the widespread clinical application of immune checkpoint inhibitors as therapeutic targets, the focus of tumor treatment has shifted from "whether there are available therapeutic drugs" to "how to select appropriate treatment strategies for different patients." Personalized treatment aims to classify patients based on their molecular biological and immunological characteristics and match them with corresponding treatment plans to maximize efficacy and reduce toxic side effects and medical costs. However, the current personalized decision-making mechanism for immunotherapy is still in its early stages. In clinical practice, a binary decision-making model based on a single biomarker (such as PD-L1 expression) is often used, judging whether to implement immunotherapy based on only a single indicator. A systematic decision-making system that can stratify patients at multiple levels and further match different treatment intensities or types has not yet been formed. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art. To this end, this application aims to provide a computer-based method and apparatus for predicting tumor treatment strategies. By constructing a multi-level stratification mechanism based on the expression ratio of immune-related antigens and establishing a mapping relationship between stratification results and treatment strategies, it achieves refined immune typing and individualized treatment strategy prediction for tumor patients, thereby improving the accuracy, safety, and utilization rate of medical resources in immunotherapy.

[0004] Specifically, the technical solution of this application is as follows: In a first aspect, this application proposes a computer-implemented method for predicting tumor treatment strategies. According to an embodiment of this application, the method includes: obtaining the proportion of target antigen-positive immune cells in the total immune cells of a target sample; classifying the target sample into antigen expression levels based on the difference between the proportion and a predetermined threshold to obtain grouping results; wherein the grouping results are any one of N preset level groups, where N is an integer not less than 1; and matching corresponding treatment strategies based on the grouping results, the treatment strategies including: administering an effective dose of an antibody corresponding to the target antigen, and / or an immune checkpoint inhibitor antibody.

[0005] Secondly, this application proposes a computer-based device for predicting tumor treatment strategies. According to embodiments of this application, the device includes: a data acquisition module, a grouping module, and a matching module.

[0006] The system includes a data acquisition module for obtaining the proportion of target antigen-positive immune cells in the total immune cells of the target sample; a grouping module for classifying the target sample into antigen expression levels based on the difference between the proportion and a predetermined threshold, and obtaining grouping results; wherein the grouping results are any of N preset level groups, where N is an integer not less than 1; and a matching module for matching corresponding treatment strategies based on the grouping results, wherein the treatment strategies include: administering an effective dose of the target antigen-corresponding antibody and / or an immune checkpoint inhibitor antibody.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory; the aforementioned memory is used to store a computer program; the aforementioned processor is used to execute the aforementioned computer program to implement the computer implementation method for predicting tumor treatment strategies as provided in the first aspect.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions or programs that, when executed on a computer, cause the computer implementation method for predicting tumor treatment strategies as provided in the first aspect to be performed.

[0009] Fifthly, embodiments of this application provide a computer program product comprising computer instructions that, when some or all of the computer instructions are executed on a computer, cause the computer implementation method for predicting tumor treatment strategies as provided in the first aspect to be executed.

[0010] In a sixth aspect, embodiments of this application provide a computer program that, when run on a computer, causes the computer to perform the computer implementation method for predicting tumor treatment strategies as provided in the first aspect.

[0011] In summary, this application provides an immunotherapy decision-making scheme based on the proportion of target antigen-positive immune cells. By obtaining the proportion of target antigen-positive immune cells in the target sample within the total immune cell population, and using this proportion as a continuous quantitative decision parameter to characterize the intensity level of the corresponding immune state in the tumor immune microenvironment, the problem of loss of stratified information caused by relying solely on a "positive / negative" dichotomy is avoided.

[0012] Based on this, according to the relationship between the ratio and the preset threshold, the antigen expression intensity of the target sample is graded, and the sample is divided into stratified groups corresponding to multiple expression level intervals, so as to realize multi-level fine immune stratification of patients and improve the discrimination and operability of treatment decisions.

[0013] Furthermore, the stratification results of different expression levels are matched with the preset treatment intervention intensity or treatment regimen type, so that each stratified sample corresponds to a different immunotherapy strategy, including but not limited to immune checkpoint inhibitors alone, immune checkpoint inhibitors combined with targeted therapy of target antigens, or targeted therapy of target antigens alone. This constructs a rule-based decision-making model of "stratification-matching-administration" based on continuous immune indicators, enabling differentiated configuration and individualized optimization of immunotherapy regimens. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a computer implementation method for predicting tumor treatment strategies according to an embodiment of this application. Figure 2 This is a schematic diagram of a computer implementation device for predicting tumor treatment strategies according to an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device according to an embodiment of this application; Figure 4 A schematic diagram of the statistical results of immune cells in the drug-resistant and non-drug-resistant groups based on multicolor immunohistochemistry provided in the embodiments of this application; Figure 5 This is a schematic diagram illustrating the proportion of SLAMF1-positive B cells in the peripheral blood of liver cancer patients and its relationship with AFP levels, provided in an embodiment of this application; where A represents the statistical results of the proportion of SLAMF1-positive B cells in the peripheral blood of liver cancer patients; and B represents the statistical results of AFP levels. Figure 6 This is a schematic diagram of an immunotherapy decision-making process based on SLAMF1 expression levels, provided as an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] The endpoints and any values ​​of the ranges disclosed herein are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of the various ranges, the endpoint values ​​of the various ranges and individual point values, and individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed herein.

[0019] In the embodiments of this application, antibodies include monoclonal antibodies, polyclonal antibodies, nanobodies, and antigen-binding fragments.

[0020] In the embodiments of this application, anti-CD150 antibody refers to an antibody that specifically recognizes and binds to the CD150 molecule. CD150, also known as SLAMF1, is a cell surface protein that is widely expressed on activated B cells, T cells, and dendritic cells.

[0021] In this application, immune checkpoint inhibitor antibodies are a class of antibodies that restore the body's anti-tumor immune response by targeting immune checkpoint molecules (such as PD-1, PD-L1, CTLA-4, LAG-3, TIGIT, etc.). Immune checkpoint molecules typically play an inhibitory role in the immune response, limiting the activity of immune cells. During tumor immune escape, tumor cells express immune checkpoint molecules that bind to receptors on the surface of immune cells, inhibiting the killing function of immune cells. Immune checkpoint inhibitor antibodies restore the tumor-killing effect by blocking these inhibitory signals, relieving the inhibitory state of immune cells.

[0022] In this embodiment, the anti-PD-1 antibody is an antibody that targets the PD-1 (programmed death receptor-1) molecule. PD-1 is an immune checkpoint receptor located on the surface of T cells. When it binds to PD-L1 on the surface of tumor cells, it can inhibit the activity of T cells, leading to immune tolerance or immune escape. The anti-PD-1 antibody enhances the body's anti-tumor immune response by binding to and blocking the PD-1 receptor, thereby restoring the function of T cells.

[0023] In this embodiment, the anti-PD-L1 antibody is an antibody that targets the PD-L1 (programmed death-ligand-1) molecule. PD-L1 is one of the common immune checkpoint molecules on the surface of tumor cells, capable of binding to the PD-1 receptor and suppressing the immune function of T cells. By binding to and blocking the PD-L1 molecule, the anti-PD-L1 antibody prevents its interaction with PD-1, thereby relieving the immunosuppressive effect of T cells and restoring the immune system's ability to recognize and eliminate tumors.

[0024] In this embodiment, the anti-CTLA-4 antibody is an antibody that targets the CTLA-4 (cytotoxic T-lymphocyte antigen-4) molecule. CTLA-4 is an inhibitory receptor on the surface of immune cells, involved in regulating T cell activity, especially in the early stages of immunity. By binding to its ligands (such as B7-1 and B7-2), CTLA-4 can inhibit T cell activation and proliferation. The anti-CTLA-4 antibody enhances T cell activity and promotes the body's anti-tumor immune response by blocking the function of the CTLA-4 receptor.

[0025] In this embodiment, the anti-LAG-3 antibody is an antibody targeting the LAG-3 (lymphocyte activation gene-3) molecule. LAG-3 is an immune checkpoint molecule mainly expressed on activated T cells, capable of binding to its ligand and inhibiting the T cell immune response. The anti-LAG-3 antibody relieves immunosuppression and enhances the anti-tumor effect of T cells by blocking the binding of LAG-3 to its ligand.

[0026] In this embodiment, the anti-TIGIT antibody is an antibody that targets the TIGIT (T cell immunoglobulin and ITIM domain) molecule. TIGIT is an immune checkpoint molecule expressed on T cells and NK cells, and participates in suppressing immune responses. The anti-TIGIT antibody relieves the immunosuppression of T cells and restores their tumor-killing effect by blocking the binding of TIGIT to its ligand.

[0027] In this application, drug-resistant tumors refer to a type of tumor that, after initial treatment, gradually loses its response to immune checkpoint inhibitors, chemotherapeutic drugs, or targeted drugs due to various immune escape mechanisms or treatment-induced drug resistance mechanisms. The formation of drug-resistant tumors is usually related to changes in tumor cells and their microenvironment, such as increased expression of immune checkpoint molecules and decline in immune cell function.

[0028] The relevant technologies generally adopt a fixed treatment plan recommended for a specific type of cancer, or a fixed combination drug model to provide uniform treatment to patients. These approaches do not provide detailed stratification based on the patient's immune-related biological characteristics, resulting in some patients not benefiting from the predetermined treatment plan, while suffering from the toxic side effects and financial burden of ineffective treatment, leading to a waste of medical resources. Furthermore, they lack guidance for subsequent treatment decisions for patients who have developed treatment tolerance or whose treatment has failed.

[0029] To address the aforementioned technical deficiencies, this application's embodiments obtain the proportion of target antigen-positive immune cells in the total immune cells of the target sample, transforming the patient's immune-related biological characteristics into quantifiable decision parameters. This shifts treatment selection from "whether it belongs to a certain type of cancer" to "what level of immune characteristics," eliminating the problem of individual differences being masked by uniform medication in related technologies. At the population level, this significantly improves the overall response rate (ORR) and progression-free survival (PFS), maximizing the utility of medical resources.

[0030] Building upon this, patients are further categorized by antigen expression level based on the difference between the stated proportion and a predetermined threshold, resulting in multiple groupings with clearly defined immune characteristics. This stratification mechanism prevents patients from being simply included in the same treatment set, but rather classifies them into different subgroups based on the intensity of immune-related antigen expression. This structurally avoids the technical approach of implementing a fixed treatment plan for all patients in related technologies, providing a necessary grouping basis for differentiated treatment decisions.

[0031] Furthermore, this application does not merely stop at the detection or stratification level, but directly uses the grouping results as the trigger condition for matching treatment strategies, automatically assigning different treatment regimens to patients with different immune expression levels. For example, matching combination therapy regimens to patients with high expression of target antigens avoids the continued use of ineffective immune checkpoint inhibitor antibody monotherapy, allowing for timely switching to potentially effective targeted therapy; while matching immune checkpoint inhibitor monotherapy regimens to patients with low expression of target antigens avoids the use of unnecessary and potentially more toxic combination therapies or antibody monotherapy targeting anti-target antigens, reducing patients' medical expenses and potential side effect risks. Through this standardized correspondence of "stratification results - treatment strategy," the treatment intensity is matched with the patient's immune characteristics, thereby avoiding overtreatment and the accumulation of toxic side effects caused by uniformly implementing combination therapy for all patients in existing technologies.

[0032] The technical solution of this application will be described in detail below: Figure 1 This is a flowchart illustrating a computer-based method for predicting tumor treatment strategies according to an embodiment of this application, with reference to... Figure 1 The method may include: S110, obtain the proportion of target antigen-positive immune cells in the total immune cells of the target sample; In an exemplary embodiment, this step is used to obtain the proportion of target antigen-positive immune cells in the total immune cells of the target sample. The target sample can be a peripheral blood sample, a tumor tissue sample, or a processed immune cell-enriched sample obtained from a cancer patient. By performing immune cell population analysis on the sample, the number of immune cells carrying the target antigen expression signal is determined, and the ratio of this number to the total number of immune cells in the sample is calculated to obtain a quantitative proportion parameter for subsequent stratification decision-making. This proportion parameter reflects the expression level of the target antigen in the immune cell population in a continuous numerical form, so that subsequent treatment decisions no longer rely solely on a single qualitative judgment of "positive / negative," but are based on stratification judgments based on continuous changes in the intensity of immune characteristics, thereby providing basic data support for constructing multi-level treatment decision-making pathways.

[0033] In an exemplary embodiment, the target antigen is selected from CD150 (SLAMF1). This application obtains the proportion of CD150-positive immune cells in the immune cell population and introduces this proportion as a continuous variable into the treatment decision-making process. This transforms the CD150 expression level from merely an auxiliary detection indicator into a core decision parameter for treatment strategy selection. By using the proportion of CD150-positive immune cells as a stratification basis, the differences in immune status among different patients are transformed into quantifiable and comparable grouping parameters. This allows for the construction of a stratified treatment pathway based on immune characteristics, enabling patients to be guided to a treatment strategy more closely matched to their immune status.

[0034] In an exemplary embodiment, the immune cells are selected from B cells, specifically CD150-positive B cells. This application, for the first time, uses the proportion of CD150-positive B cells in the total B cells as a core node for treatment decision-making. This allows patient stratification to move beyond relying on a single cancer type or fixed medication regimen, instead using multi-branch triage based on the status of immune cell subsets closely related to humoral immunity and the tumor immune microenvironment. By stratifying patients based on this proportion and matching different stratification results to different treatment strategies, patients can be precisely triaged to the treatment plan most likely to benefit them. This ensures efficacy while avoiding unnecessary high-intensity immune interventions for patients with low immune profiles who may not require combination therapy, reducing the risk of toxic side effects and improving the efficiency of medical resource utilization.

[0035] S120, based on the difference between the ratio and a predetermined threshold, the target sample is divided into antigen expression levels to obtain grouping results; wherein, the grouping results are any one of the preset N level groups, and N is an integer not less than 1; In an exemplary embodiment, this step is used to classify the target sample into N level groups based on the difference between the proportion obtained in step S110 and a predetermined threshold. In a specific implementation, the N level groups can be a CD150 antigen high-expression group and a CD150 antigen low-expression group; or a CD150 antigen high-expression group, a CD150 antigen moderate-expression group, and a CD150 antigen low-expression group; or a CD150 antigen extremely high-expression group, a CD150 antigen high-expression group, a CD150 antigen moderate-expression group, a CD150 antigen low-expression group, and a CD150 antigen extremely low-expression group, etc.

[0036] In an exemplary embodiment, the preset N-level grouping in this step includes at least a CD150 antigen high-expression group and a CD150 antigen low-expression group. When the proportion is not lower than the predetermined threshold, the target sample is classified as the CD150 antigen high-expression group; when the proportion is lower than the predetermined threshold, the target sample is classified as the CD150 antigen low-expression group. By introducing this grouping mechanism, patients are no longer simply divided based on "whether antigen expression is detected," but rather structured stratification is performed based on the expression ratio of immune-related antigens in the immune cell population, thereby distinguishing patient subgroups with significantly different immune function statuses at the level of immune expression.

[0037] In an exemplary embodiment, the predetermined threshold is obtained by statistically analyzing the proportion of target antigen-positive immune cells in the total immune cells in the training sample, and the median of the proportion is used as the predetermined threshold. By using the median as the grouping threshold, the resulting CD150 antigen high-expression group and CD150 antigen low-expression group have statistical balance in sample distribution, thereby ensuring the distinguishability of the two subgroups in terms of immune expression levels, while avoiding the interference of extreme values ​​on the grouping results.

[0038] For training samples with different data volumes or differences in cancer types, those skilled in the art can adaptively adjust the aforementioned predetermined threshold. In specific embodiments, the predetermined threshold is selected from 30%-50%, and can optionally be 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, or 50%. In a preferred embodiment, the predetermined threshold is 40%.

[0039] S130, based on the grouping results, a corresponding treatment strategy is matched, the treatment strategy including: administering an effective dose of the target antigen-corresponding antibody and / or an immune checkpoint inhibitor antibody.

[0040] In an exemplary embodiment, this step is used to match the target sample with a corresponding treatment strategy based on the grouping results obtained in step S120. By using the grouping results as a direct input parameter for treatment decisions, the selection of treatment strategies no longer relies on a single empirical judgment, but is driven by the quantitative immune feature of the target antigen expression level, thereby constructing a rule-based treatment path based on the immune stratification results.

[0041] In an exemplary embodiment, when the grouping result is a high CD150 antigen expression group, the matched treatment strategy is a combination therapy of an effective dose of anti-CD150 antibody and an immune checkpoint inhibitor antibody. Since the target sample has a high proportion of CD150 expression in the immune cell population, it indicates the existence of an immune pathway that can be targeted and regulated by CD150 in its immune microenvironment. By introducing an anti-CD150 antibody on top of an immune checkpoint inhibitor antibody for synergistic intervention, the treatment specificity for this subgroup of patients can be improved while enhancing the immune activation effect, thereby avoiding the potential for insufficient efficacy when using only immune checkpoint inhibitor monotherapy for this type of patient.

[0042] In an exemplary embodiment, the inventors unexpectedly discovered that for target subjects who developed resistance after treatment with immune checkpoint inhibitor antibodies, as identified by pathological images, the proportion of CD150-positive immune cells in the immune cell population within their tumor tissue was higher than the predetermined threshold of this application's embodiments. Co-treatment with anti-CD150 antibodies and immune checkpoint inhibitor antibodies overcame immune resistance and produced a synergistic therapeutic effect, significantly enhancing the efficacy of immunotherapy. This also provides clinicians with a biologically based, operational decision-making framework (“detection-re-decision”) for managing challenging patients resistant to immune checkpoint inhibitors, filling a gap in clinical practice.

[0043] In an exemplary embodiment, when the grouping result is a low CD150 antigen expression group, the matched treatment strategy is a monotherapy regimen of administering an effective dose of an immune checkpoint inhibitor antibody. Because the proportion of CD150 expression in the immune cell population of this group is low, their potential response to anti-CD150 antibodies is relatively limited. By treating with only immune checkpoint inhibitor antibodies, the efficacy of basic immunotherapy can be guaranteed while avoiding the introduction of additional targeted drugs that may have limited efficacy against this subgroup, thereby reducing the risk of immune-related toxic side effects and unnecessary treatment costs.

[0044] In exemplary embodiments, the treatment regimens for the CD150 antigen low expression group, CD150 antigen high expression group, or immune checkpoint inhibitor resistance group can all be supplemented with other therapeutic drugs (such as cytokines) to enhance the treatment effect based on the treatment regimens of the embodiments of this application.

[0045] In an exemplary embodiment, the effective dose of the anti-CD150 antibody can be selected from a dose range of 1 mg / kg to 10 mg / kg based on body weight; the effective dose of the immune checkpoint inhibitor antibody can be selected from 2 mg / kg to 4 mg / kg based on body weight, or administered at a fixed dose, selected from 200 mg / time to 400 mg / time. By parameterizing the dosage, the treatment strategy can be adapted to patients of different body types and clinical medication habits, improving the feasibility and versatility of the treatment regimen.

[0046] In an exemplary embodiment, the treatment strategy further includes a dosing frequency parameter, preferably administered once every 3 to 4 days. By determining the dosing frequency, the treatment intervention can maintain immune activation while avoiding overstimulation of the immune system, thereby achieving a balance between efficacy and safety.

[0047] The embodiments described above in this application can also be used for dynamic monitoring during treatment to adjust the treatment plan in a timely manner. For example, during treatment, changes in the proportion of CD150-positive B cells can be used to predict treatment efficacy and adjust treatment strategies. For example, if the initial treatment uses monotherapy with an immune checkpoint inhibitor antibody, and the proportion of CD150-positive B cells exceeds a predetermined threshold during treatment, it indicates that the target subject has developed resistance to the immune checkpoint inhibitor antibody, and combination therapy is required. Alternatively, if the proportion of CD150-positive B cells decreases below a predetermined threshold during treatment, the dosage can be reduced.

[0048] The embodiments described above in this application can also be used to make a comprehensive judgment based on the proportion of CD150-positive B cells, combined with information from other biomarkers, to increase the accuracy of decision-making. The aforementioned other biomarker information includes, but is not limited to, information on the expression of immune checkpoint inhibitors, tumor mutational burden (TMB), and information on the expression of inflammatory genes.

[0049] Through the above embodiments, this application constructs a differentiated treatment strategy matching mechanism based on CD150 expression stratification results, enabling patients with different immune expression levels to receive different treatment intensities and combinations, thereby avoiding the overtreatment problem caused by uniformly applying combined immunotherapy to all patients in the prior art, and improving the accuracy, safety and resource utilization efficiency of treatment decisions.

[0050] Furthermore, this application achieves a standardized connection between the detection process and the treatment plan by directly using the CD150 detection results as the decision-making basis for treatment strategy selection. This makes the quantitative detection of the target antigen no longer just an auxiliary reference information, but a key input parameter for treatment path selection. Thus, a complete technical process from immune feature detection to treatment strategy matching is constructed, which improves the systematicness, operability and clinical application value of immunotherapy decision-making.

[0051] The above text combined Figure 1 This document describes an embodiment of a computer-based method for predicting tumor treatment strategies according to this application. The following is in conjunction with... Figure 2 An embodiment of the apparatus described in this application is presented.

[0052] Figure 2 This is a schematic block diagram of a computer implementation device 200 for predicting tumor treatment strategies provided in an embodiment of this application, wherein the device 200 can be configured in an electronic device.

[0053] refer to Figure 2 The computer implementation device 200 for predicting tumor treatment strategies includes: a data acquisition module 210, a grouping module 220, and a matching module 230.

[0054] The data acquisition module 210 is used to acquire the proportion of target antigen-positive immune cells in the total immune cells of the target sample; the grouping module 220 is used to classify the target sample into antigen expression levels based on the difference between the proportion and a predetermined threshold, and obtain grouping results; wherein the grouping results are any of N preset level groups, where N is an integer not less than 1; the matching module 230 is used to match the corresponding treatment strategy based on the grouping results, wherein the treatment strategy includes: administering an effective dose of the target antigen-corresponding antibody and / or an immune checkpoint inhibitor antibody.

[0055] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be found in the method embodiments. To avoid repetition, further details are omitted here. Specifically, Figure 2 The device 200 shown can perform Figure 1 The corresponding method embodiments, and the foregoing and other operations and / or functions of each module in the device 200 are respectively implemented to achieve Figure 1 For the sake of brevity, the corresponding processes in each method are not described in detail here.

[0056] The apparatus 200 of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this application can be completed by integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this application embodiment can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0057] Figure 3 This is a schematic block diagram of the electronic device 300 provided in an embodiment of this application. The electronic device 300 may be the aforementioned training device or execution device, but is not limited thereto. Figure 3 As shown, the electronic device 300 may include: The system includes a memory 310 and a processor 320. The memory 310 stores a computer program 330 and transfers the computer program 330 to the processor 320. In other words, the processor 320 can retrieve and run the computer program 330 from the memory 310 to implement the methods described in the embodiments of this application.

[0058] For example, the processor 320 can be used to execute the steps in the above method according to the instructions in the computer program 330.

[0059] In some embodiments of this application, the processor 320 may include, but is not limited to: General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0060] In some embodiments of this application, the memory 310 includes, but is not limited to: Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0061] In some embodiments of this application, the computer program 330 may be divided into one or more modules, which are stored in the memory 310 and executed by the processor 320 to perform the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 330 in the electronic device.

[0062] like Figure 3 As shown, the electronic device 300 may further include: Transceiver 340, which can be connected to processor 320 or memory 310.

[0063] The processor 320 can control the transceiver 340 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 340 may include a transmitter and a receiver. The transceiver 340 may further include antennas, and the number of antennas may be one or more.

[0064] It should be understood that the various components in the electronic device 300 are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0065] According to one aspect of this application, a computer-readable storage medium is provided that stores computer instructions or programs thereon, which, when executed by a computer, enable the computer to perform the methods of the above-described method embodiments. Alternatively, embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods of the above-described method embodiments.

[0066] According to another aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described in the above-described method embodiments.

[0067] In other words, when implemented using software, it can be implemented wholly or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0068] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0070] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; 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 implement the solution of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0071] The embodiments of this application will now be described in more detail, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. Reagents or instruments used, unless otherwise specified, are all commercially available conventional products.

[0072] Example 1: Association analysis between SLAMF1-related immunophenotype and PD-1 inhibitor treatment response This embodiment uses multiplex immunofluorescence (mIF) technology to detect the spatial distribution and expression characteristics of different immune cell subsets in hepatocellular carcinoma tissue sections, in order to analyze the association between SLAMF1-related immunophenotype and PD-1 inhibitor treatment response status.

[0073] I. Preparation of Experimental Materials and Reagents Hepatocellular carcinoma tissue samples fixed with 4% paraformaldehyde and embedded in paraffin were selected, and tissue sections with a thickness of 4 μm were prepared and attached to anti-detachment glass slides. The sections were then baked overnight at 60 °C for later use.

[0074] Key reagents used in the experiment include: a fluorescence signal amplification kit, which contains at least HRP polymer secondary antibody, fluorescently labeled tyrosine, and antibody elution / antigen retrieval solution; a series of primary antibodies targeting different targets (including but not limited to SLAMF1, CD20, CD19, CD8, IFN-γ, and CD45, all of which have been pre-validated); 10% neutral formalin, antigen retrieval solution, DAPI nuclear dye, anti-fluorescence quenching mounting medium, blocking solution, and TBST buffer.

[0075] The main experimental equipment includes a microwave oven, a temperature-controlled shaker, a histochemistry pen, a humidification chamber, and a fluorescence microscopy imaging system.

[0076] II. Multiplex fluorescence immunohistochemical detection process (a) Dewaxing, hydration and post-fixation treatment Tissue sections were dewaxed sequentially in fresh xylene I, II, and III for 10 minutes each time; then hydrated sequentially in 100%, 95%, and 70% ethanol for 5 minutes each. After hydration, the sections were washed three times with sterile deionized water for 1 minute each time.

[0077] To further stabilize the tissue antigen structure, the slides were immersed in 10% neutral formalin for 10 minutes and then washed three times with sterile deionized water for one minute each time.

[0078] (II) Antigen retrieval The processed slides were placed in a retrieval chamber containing preheated antigen retrieval solution (such as EDTA buffer at pH 9.0), and antigen retrieval was performed using microwave heating. Specifically, the solution was heated to boiling on high heat, then reduced to low heat and maintained at a gentle boil for 15 minutes, ensuring that the tissue was fully immersed in the retrieval solution throughout the process. After retrieval, the retrieval chamber was allowed to cool naturally to room temperature.

[0079] (III) Sealing off After aspirating excess liquid from the slide surface, draw a hydrophobic zone around the tissue using a histochemical pen, and add sufficient blocking solution to completely cover the tissue area. Place the slide in a humidified chamber and incubate at room temperature for 20 minutes to block non-specific binding sites.

[0080] (iv) First round of primary antibody incubation and signal amplification After discarding the blocking solution, add the working solution of the first target primary antibody (e.g., anti-SLAMF1 antibody) directly to the tissue area and incubate at low speed on a shaker at 37 °C for 1 hour. After incubation, wash the slides three times with TBST buffer for 5 minutes each time.

[0081] Subsequently, HRP-labeled polymer secondary antibody matching the species of the primary antibody was added, and the mixture was incubated at room temperature for 30 minutes. After washing, the first fluorescently labeled tyrosine working solution was added, and the mixture was incubated at room temperature in the dark for 10 minutes to complete signal deposition. The mixture was then washed three times with TBST buffer in the dark.

[0082] (v) Antibody elution and multiple rounds of cyclic staining After the first round of fluorescence signal deposition, the slides were immersed in antibody elution / retrieval buffer and eluted using a microwave heating method. Specifically, the slides were boiled on high heat and then kept on low heat for 10–15 minutes to dissociate the previous primary / secondary antibody complex and inactivate HRP activity. After elution, the slides were allowed to cool naturally to room temperature and washed with TBST buffer.

[0083] After completing the above elution steps, repeat the blocking, primary antibody incubation, secondary antibody incubation, fluorescence signal deposition and elution process, changing the primary antibody to different targets and the corresponding different fluorescence channels, until the detection of all target immunomarkers is completed.

[0084] (vi) Nuclear counterstaining and mounting After the final round of staining and elution, the sections were counterstained with cell nuclei. DAPI working solution was added to the tissue area and incubated at room temperature in the dark for 5 minutes, followed by washing three times with TBST buffer in the dark. After a brief rinse with sterile deionized water, excess water was aspirated, and an anti-fluorescence quenching mounting medium was added to the tissue area. The area was carefully covered with a coverslip and the edges were sealed. The sections were stored in the dark at 4 °C.

[0085] III. Image Acquisition and Data Analysis Multichannel fluorescence images of stained tissue sections were acquired using the AKOYA PhenoImager Fusion imaging system. The obtained images were analyzed using QuPath software, including multichannel signal overlay, fluorescence co-localization analysis, and quantitative statistics of different immune cell phenotypes.

[0086] IV. Results Analysis Multiplex fluorescence immunohistochemistry results as follows Figure 4 As shown, a comparison of tumor tissues from hepatocellular carcinoma patients in the PD-1 inhibitor resistance group (PD) and the non-PD-1 inhibitor resistance group (PR) revealed that CD20 infiltrates in the tumor in the PD-1 resistance group. + SLAMF1 + The number of cells increased significantly, while CD8... + IFN-γ + The number of effector T cells was relatively reduced. This result indicates a correlation between the enrichment of SLAMF1-associated immune cells and PD-1 treatment resistance phenotype.

[0087] Example 2: Prediction of hepatocellular carcinoma immunotherapy based on SLAMF1 expression level in peripheral blood B cells This embodiment uses peripheral blood samples from patients with hepatocellular carcinoma (HCC) as the research object. By quantitatively detecting the expression ratio of SLAMF1 in B cells in peripheral blood mononuclear cells (PBMCs), the patient's immunotherapy stratification and treatment plan matching can be achieved.

[0088] I. Separation of PBMCs (density gradient centrifugation) Peripheral anticoagulated blood samples were collected from patients with hepatocellular carcinoma or healthy controls. First, the anticoagulated blood samples were centrifuged at 500 × g for 5–10 minutes, and the supernatant plasma was aspirated and discarded. Then, the remaining anticoagulated whole blood was gently mixed with an equal volume of phosphate-buffered saline (PBS) to obtain a diluted blood sample.

[0089] Take a 15 mL centrifuge tube and add 3-4 mL of lymphocyte separation medium to the bottom. Slowly spread the diluted blood along the tube wall onto the surface of the separation medium to form a clear liquid stratification structure. Place the centrifuge tube in a horizontal rotor centrifuge and centrifuge at 750 × g for 30 minutes at room temperature, with the ascending speed set to 1 and the descending speed set to 0, to obtain a clear white film layer.

[0090] After centrifugation, carefully aspirate the white membrane layer located at the interface between the plasma layer and the separation solution using a Pasteur pipette and transfer it to a new 15 mL centrifuge tube. Add 10 mL of PBS to the centrifuge tube, mix well, centrifuge at 500 × g for 5 minutes, discard the supernatant, and gently tap the bottom of the tube to loosen the cell pellet.

[0091] When there are a lot of residual red blood cells in the sample, red blood cell lysis can be performed: add 2 mL of 1× red blood cell lysis buffer to the cell pellet and incubate at room temperature in the dark for 5 minutes; then add 10 mL of PBS to stop the lysis reaction, and centrifuge at 500× g for 10 minutes and discard the supernatant.

[0092] Finally, the PBMC cells were resuspended in PBS and counted to adjust the cell concentration to 1 × 10^7 cells / mL for subsequent detection or cryopreservation.

[0093] II. Staining of cell surface markers Freshly isolated or resuscitated PBMCs were washed with PBS and centrifuged to obtain a cell pellet. The cells were resuspended in 100 μL PBS, and pre-diluted viable dye (FVS) at a ratio of 1:1000 was added. After mixing, the cells were incubated at room temperature in the dark for 15 minutes. Then, 100 μL of fetal bovine serum (FBS) was added, and the cells were incubated at room temperature for 5 minutes to terminate the staining reaction.

[0094] Add 1 mL of PBS to the stained cells, centrifuge at 500 × g for 5 minutes, and discard the supernatant. Then, use mouse serum diluted 1:10 as the blocking solution, resuspend the cells in 100 μL of the blocking solution, and incubate on ice for 15 minutes to block nonspecific binding.

[0095] After blocking, a surface antibody mixture containing CD45, CD19, and SLAMF1 antibodies was added to the cells, gently mixed, and incubated at 4 °C in the dark for 30 minutes. After staining, 2 mL of PBS was added, centrifuged at 500 × g for 5 minutes, the supernatant was discarded, and the cells were washed once more. Finally, the cells were resuspended in 100-200 μL of PBS for flow cytometry analysis.

[0096] III. Flow Cytometry Detection and Data Analysis Flow cytometry was used to analyze the stained cells, and the data were analyzed according to the following gating strategy: Cell populations were delineated in the FSC-A and SSC-A scatter plots to exclude debris signals; cell aggregates were then excluded in the FSC-A and FSC-H plots; and viable cell populations were delineated using the negative signal of viability dyes.

[0097] In a live cell population, a population of lymphocytes with high CD45 expression was delineated based on SSC-A and CD45 expression levels; further, within this lymphocyte population, a B cell subset was delineated based on CD19 positive signals; finally, CD19... + The proportion of SLAMF1-positive cells was analyzed in B cells.

[0098] The results are as follows Figure 5 As shown, statistical analysis of PBMC samples (HD) from healthy individuals revealed that CD19 + SLAMF1 + The proportion of SLAMF1-positive B cells in B cells is less than 40%. In peripheral blood samples (HCC) from patients with hepatocellular carcinoma, the proportion of SLAMF1-positive B cells shows individual variability, with a statistical median of approximately 40%. Figure 5 (A) Further analysis of the patients' clinicopathological information revealed that patients with high SLAMF1 expression often had elevated alpha-fetoprotein (AFP) levels, suggesting that high SLAMF1 expression is associated with poor prognosis. Figure 5 (B in the middle).

[0099] IV. Treatment Decision-Making Process and Stratification Based on the above results, this embodiment constructs an immunotherapy decision-making process based on SLAMF1 expression levels, the overall process of which is as follows: Figure 6 As shown in the diagram, the process, from left to right, includes: confirmation of hepatocellular carcinoma diagnosis, quantitative detection of SLAMF1 expression levels in peripheral blood B cells, risk stratification based on preset thresholds, and development of individualized treatment plans.

[0100] Specifically, when the proportion of SLAMF1-positive B cells is ≥40%, patients are defined as SLAMF1 high-expressing individuals, indicating a high risk of resistance to PD-1 inhibitor treatment; when the proportion of SLAMF1-positive B cells is <40%, patients are defined as SLAMF1 low-expressing individuals, indicating a low risk of resistance to PD-1 inhibitor treatment.

[0101] Based on the above stratification results, corresponding treatment plans are matched for different patients: for patients with high SLAMF1 expression, a combination therapy of PD-1 inhibitor and SLAMF1 targeted therapy is recommended; for patients with low SLAMF1 expression, a PD-1 inhibitor monotherapy regimen is recommended.

[0102] Using the above methods, a stratified treatment decision-making pathway based on continuous quantitative detection of immune biomarkers was constructed, enabling precise and individualized configuration of immunotherapy strategies for hepatocellular carcinoma.

[0103] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0104] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0105] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A computer-based method for predicting tumor treatment strategies, characterized in that, include: Obtain the proportion of target antigen-positive immune cells in the total immune cells of the target sample; Based on the difference between the ratio and a predetermined threshold, the target samples are divided into antigen expression levels to obtain grouping results; wherein, the grouping results are any of the preset N level groups, and N is an integer not less than 1; Based on the grouping results, a corresponding treatment strategy is matched, which includes: administering an effective dose of the target antigen-corresponding antibody and / or an immune checkpoint inhibitor antibody.

2. The method according to claim 1, characterized in that, include: The target antigen is selected from CD150.

3. The method according to claim 2, characterized in that, The N-level groupings include: CD150 antigen high expression group and CD150 antigen low expression group; Wherein, the CD150 antigen high expression group is defined as a proportion not lower than a predetermined threshold; the CD150 antigen low expression group is defined as a proportion lower than a predetermined threshold.

4. The method according to claim 3, characterized in that, The matching of corresponding treatment strategies based on the grouping results includes: If the grouping result is a CD150 antigen high expression group, the treatment strategy is to administer an effective dose of anti-CD150 antibody and immune checkpoint inhibitor antibody; If the grouping result is a low expression group of CD150 antigen, the treatment strategy is to administer an effective dose of immune checkpoint inhibitor antibody; Preferably, the effective dose of the anti-CD150 antibody, based on body weight, is selected from 1 mg / kg to 10 mg / kg; Preferably, the effective dose of the immune checkpoint inhibitor antibody, on a body weight basis, is selected from 2 mg / kg to 4 mg / kg; Preferably, the effective dose of the immune checkpoint inhibitor antibody is selected from 200 mg / dose to 400 mg / dose, measured in fixed doses.

5. The method according to any one of claims 1-4, characterized in that, The treatment strategy includes: frequency of drug administration; Preferably, the administration frequency is once every 3-4 days.

6. The method according to any one of claims 1-4, characterized in that, The immune cells are selected from B cells.

7. The method according to any one of claims 1-4, characterized in that, The predetermined threshold is obtained by statistically analyzing the median proportion of target antigen-positive immune cells in the training samples.

8. A computer-based device for predicting tumor treatment strategies, characterized in that, The device includes: The data acquisition module is used to obtain the proportion of target antigen-positive immune cells in the total immune cells of the target sample; The grouping module is used to classify the target sample into antigen expression levels based on the difference between the ratio and a predetermined threshold, and obtain grouping results; wherein, the grouping results are any one of the preset N level groups, and N is an integer not less than 1; A matching module is used to match corresponding treatment strategies based on the grouping results, the treatment strategies including: administering an effective dose of a target antigen-corresponding antibody and / or an immune checkpoint inhibitor antibody.

9. An electronic device, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is configured to execute the computer program to implement the computer implementation method for predicting tumor treatment strategies as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions or programs that, when executed on a computer, cause the computer implementation method for predicting tumor treatment strategies as described in any one of claims 1 to 7 to be performed.

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