A method and system for dynamically updating an antibody composition based on time series antigen information and application

Abstract

The present application relates to the technical field of biological medicine and tumor immunotherapy, and particularly relates to a dynamic updating method and system of antibody composition based on time sequence antigen information and application thereof, which comprises the following steps: (1) obtaining tumor related antigen samples from tumors at different time points; (2) obtaining a plurality of batches of antibody sets corresponding to different time points; (3) constructing antibody compositions for each time point respectively; (4) dynamically updating the antibody composition constructed for the previous time point based on the time sequence, so as to obtain an antibody composition adapted to the current tumor antigen characteristics; (5) repeatedly executing steps (1) to (4) at new different time points based on the time sequence, so as to obtain a dynamically updated antibody composition adapted to the current tumor antigen characteristics. The present application solves the problem of tumor immunotherapy failure caused by antigen loss, increase or drift and other problems in the prior art.

Description

Technical Field

[0001] This invention relates to the fields of biomedicine and tumor immunotherapy, specifically to a method, system, and application for dynamically updating antibody compositions based on time-series antigen information. Background Technology

[0002] Tumor immunotherapy, such as CAR-T and immune checkpoint inhibitors, relies on the recognition of specific tumor antigens. However, tumor cells exhibit significant heterogeneity and evolutionary capacity, and are prone to problems such as antigen loss or increase, the emergence of new antigens, changes in antigen structure (antigen drift), or decreased antigen presentation capacity under conditions such as natural evolution or treatment stress.

[0003] Currently, existing technologies for anti-tumor immunotherapy or antibody preparation mainly include monoclonal antibody technology, polyclonal antibody technology, CAR-T cell therapy, and vaccine or antibody design methods based on fixed antigen targets. These technologies can achieve the purpose of tumor inhibition or immune recognition to a certain extent, but they all have the following limitations: (1) They rely on pre-determined fixed antigen targets and lack the ability to adapt to dynamic changes in tumor antigens; (2) Existing multi-antibody combination schemes are usually statically designed and cannot be structurally adjusted according to the tumor progression process; (3) CAR-T and cell therapy mainly rely on T cell recognition mechanisms and are limited by antigen loss and changes in antigen presentation ability; (4) Existing serum or polyclonal antibody schemes are mostly generated in one go and lack a systematic update mechanism and antibody evolution path.

[0004] However, the therapeutic effect decreases significantly when tumor antigens change. Although multi-antibody or multi-target strategies exist, they are still static designs and lack mechanisms to adjust with tumor evolution. Therefore, there is an urgent need for a technical solution that can dynamically adjust the composition of therapeutic antibodies according to changes in tumor antigens. In view of this, the present invention provides a method, system, and application for dynamically updating antibody compositions based on time-series antigen information. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method, system, and application for dynamically updating antibody compositions based on time-series antigen information. The aim is to solve the problem of treatment failure caused by antigen loss, addition, or drift in the prior art.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, a method for dynamically updating antibody compositions based on time-series antigen information includes the following steps: (1) Obtain tumor-related antigen samples from tumors at different time points to obtain multiple batches of antigens at different time points that reflect the changes of tumor antigens over time; (2) Antibodies were induced by multiple batches of the antigen at different time points to obtain antibody sets at different time points; (3) Screen and enrich antibody compositions that can specifically bind to tumor-associated antigens at corresponding time points from the antibody sets of each batch, and construct antibody compositions targeting each time point respectively; (4) Based on the time series, the antibody composition constructed at the previous time point is dynamically updated according to the tumor antigen characteristics at least at the later time point to obtain an antibody composition adapted to the current tumor antigen characteristics. (5) Repeat steps (1) to (4) sequentially at new different time points based on the time series to obtain a dynamically updated antibody composition adapted to the current tumor antigen characteristics.

[0007] Obtaining tumor-associated antigen samples from tumors at different time points refers to taking samples from tumor tissues, obtaining drug compositions, and ultimately creating a database for treating tumor-related diseases, which can then be used to treat cancer patients at different stages.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the tumor-associated antigens mentioned in step (1) include, but are not limited to: whole tumor cell components; tumor cell membrane-associated protein components; and mixtures of tumor-derived proteins or peptides. The different time points are used to reflect changes in tumor antigens as the disease progresses or under treatment stress.

[0010] Furthermore, the method of inducing antibody production in step (2) includes at least one of in vitro antibody production and in vivo antibody production. The in vitro antibody generation method includes: culturing the tumor-associated antigen in contact with immune cells derived from a healthy donor in vitro, and inducing B cells to produce antibodies under immune stimulation conditions; wherein the immune stimulation conditions include at least one of cytokine stimulation, co-stimulatory molecule activation, or immune agonist stimulation. The method of generating antibodies in vivo includes: introducing the tumor-associated antigen into a non-tumor body, inducing an immune response in the body, and then obtaining antibodies.

[0011] It should be noted that the antibody generation step is repeated based on tumor antigen information at different time points, thereby obtaining a set of antibodies targeting different antigen characteristics.

[0012] Furthermore, the antibody screening method in step (3) includes at least one of antigen binding capacity screening, antibody display technology screening, and polyclonal composition analysis based on antibody sequence information. The screening and enrichment steps can be performed separately based on tumor antigens acquired at different time points to obtain an antibody set that changes over time.

[0013] Further, the antibody composition in step (3) is at least one of a polyclonal antibody mixture, an oligoclonal antibody composition, and a recombinant expression antibody composition; the antibody composition contains multiple antibodies with different antigen-binding properties, used to simultaneously recognize multiple different antigenic determinants on the surface of tumor cells. The antibody composition is used to simultaneously recognize multiple different antigenic determinants on the surface of tumor cells to improve coverage of tumor heterogeneity. The antibody composition can be updated or reconstructed based on antibody sets obtained at different time points.

[0014] Furthermore, the dynamic update described in step (4) includes at least one of the following operations (a) to (c): (a) Replacing a portion of the antibody in the antibody composition; (b) Increase antibodies against newly emerging or upregulated antigens; (c) Delete or reduce the proportion of antibodies against antigens whose expression levels are downregulated or have disappeared. The update is based on information about changes in tumor antigens at different time points, enabling the antibody composition to continuously adapt to the dynamic changes in tumor antigens. The update process can be repeated multiple times to form an antibody composition sequence that evolves over time.

[0015] Furthermore, the method includes an antibody library establishment step: sequencing the obtained antibody sets from multiple batches at different time points and establishing an antibody database; the antibody database includes at least antibody sequence information, antigen binding characteristic information, and antigen source information at the corresponding time points. The antibody database is used to guide subsequent antibody screening, combination, dynamic updating, or optimization.

[0016] Secondly, an antibody dynamic updating system for implementing the aforementioned antibody composition dynamic updating method includes: S1: Antigen acquisition module, used to acquire tumor-related antigen samples from tumors at different time points, and obtain multiple batches of antigens at different time points that reflect the changes of tumor antigens over time; S2: Antibody generation module, used to induce antibody production using multiple batches of the antigen at different time points to obtain a set of multiple batches of antibodies corresponding to different time points; S3: Antibody screening and enrichment module, used to screen and enrich antibody compositions that can specifically bind to tumor-associated antigens at corresponding time points from the antibody sets in each batch, and to construct antibody compositions targeting each time point respectively; S4: Antibody composition construction module, used to dynamically update the antibody composition constructed at the previous time point based on the tumor antigen characteristics at least at the later time point according to the time series, so as to obtain an antibody composition adapted to the current tumor antigen characteristics; S5: Dynamic update decision module, used to repeat steps (1) to (4) sequentially at new different time points based on time series, to obtain a dynamically updated antibody composition adapted to the current tumor antigen characteristics.

[0017] Furthermore, the adjustments performed by the dynamic update decision module include replacing some antibodies in the antibody composition, increasing antibodies against newly emerging or upregulated antigens, and deleting or reducing the proportion of antibodies against antigens whose expression levels are downregulated or have disappeared.

[0018] Thirdly, an antibody composition based on time-series antigen information is prepared by the aforementioned antibody composition dynamic update method, or an antibody composition updated by the aforementioned antibody dynamic update system.

[0019] Note: This antibody composition based on time-series antigen information is ultimately used to create different drugs, which are then used to treat cancer patients at different stages of their disease.

[0020] Fourthly, the method for dynamically updating antibody compositions based on time-series antigen information, the antibody dynamic updating system, or the application of the antibody composition in the preparation of drugs for treating tumors.

[0021] This invention enables the therapeutic target to be dynamically updated through time-series antigen collection; improves the diversity of antibody sources through in vitro or in vivo antibody generation pathways; enhances the coverage of tumor heterogeneity through multi-antibody combination structures; and enables the antibody composition to adapt to changes in tumor antigens through a periodic update mechanism, thereby reducing the risk of treatment failure due to problems such as antigen loss, increase, or drift. At the same time, the various technical means work together to form a dynamic immunotherapy system that can continuously adapt to tumor evolution.

[0022] In this invention, 'antigen loss, increase, or drift' refers to tumor cells where point mutations, deletions, or insertions, downregulation or increase in expression, the appearance of new antigens, or structural changes occur in surface antigen epitopes, rendering existing antibodies or immune cells unable to effectively recognize and bind to them. This includes, but is not limited to: substitution of amino acid residues within antigen epitopes, decrease or increase in antigen expression levels, redistribution of antigens from the cell membrane, and abnormalities in antigen presentation pathways.

[0023] The beneficial effects of this invention are: (1) Achieving dynamic adaptation capability of antitumor antibody compositions: Compared with existing tumor immunotherapies that mostly use pre-set targets or fixed antibody combinations, which cannot be adjusted according to changes in tumor antigens during treatment and are prone to decreased efficacy due to problems such as antigen loss, increase or drift, this invention obtains tumor-related antigens repeatedly at different time points and prepares and updates antibody compositions accordingly. This allows the antibody compositions to be dynamically adjusted according to changes in tumor antigen characteristics, thereby achieving the responsive adjustment capability of the antibody compositions to changes in tumor antigens.

[0024] (2) Improved coverage of tumor heterogeneity: This invention constructs an antibody composition consisting of multiple antibodies with different antigen-binding properties, which enables simultaneous recognition of multiple antigenic determinants on the surface of tumor cells. Compared with single-target antibodies or fixed multi-antibody combinations, it can improve the coverage of tumor heterogeneity to a certain extent.

[0025] (3) Reduce the risk of single antigen target failure: Compared with the fact that single target therapy is prone to failure due to the loss of antigens or structural changes that may occur in tumor cells under natural evolution or treatment pressure, the present invention reduces the dependence of antibody therapy on a single antigen target by combining multiple antibodies and dynamic antibody renewal mechanism.

[0026] (4) Introducing a time-based antibody update mechanism: This invention introduces a "time-series antigen information driven" update mechanism into the antibody preparation system for the first time, so that the antibody composition is no longer a static product, but a dynamic system that can be structurally adjusted according to the tumor antigen characteristics at different time points, thereby distinguishing it from existing fixed antibody or fixed antibody combination technology and realizing the structural iterative update of the antibody composition based on time-series antigen information.

[0027] (5) Improve antibody source diversity and screening flexibility: The present invention can obtain antibody sources by in vitro immunization, in vivo immunization or a combination of both. Compared with a single antibody production method, it can improve antibody diversity under different implementation paths and enhance the flexibility of antibody screening and optimization.

[0028] (6) Constructing a traceable antibody information system: This invention enables the recording and tracking of antibody sources, structural characteristics, and corresponding antigen information by performing sequence analysis on antibodies and establishing an antibody database. This provides a data foundation for subsequent antibody optimization and combination strategy adjustments, improving the controllability and reproducibility of the antibody development process. The antibody database is used to support the screening and dynamic updating of antibody compositions.

[0029] (7) Enhance the engineering capability of systematic antibody therapy: This invention modularizes the antigen acquisition, antibody generation, screening, combination and updating process, making the entire antibody preparation and updating process systematic and scalable, which is conducive to the subsequent large-scale preparation and engineering application, forming an antibody generation and updating system that can be executed in a closed loop.

[0030] (8) The technical solution described in this invention has universality across tumor types. Although human liver cancer tissue is used as the antigen source and mouse squamous cell carcinoma model of skin is used as the treatment verification in the embodiments, the core mechanism of this invention is the dynamic updating of antibodies based on time-series antigen information, which does not depend on specific tumor types or species. Those skilled in the art can reasonably expect that the same method can be applied to other solid tumors, including but not limited to lung cancer, colorectal cancer, breast cancer, melanoma, gastric cancer, esophageal cancer, pancreatic cancer and head and neck squamous cell carcinoma, etc. The implementation of the technical solution in different tumor types only requires replacing the corresponding tumor antigen source, without creative labor. Therefore, the scope of protection of this invention covers all tumor treatment applications that adopt this dynamic updating mechanism. Attached Figure Description

[0031] Figure 1 This is the overall flowchart of the antibody dynamic modification system of the present invention; Figure 2 This is a structural diagram of the integration of antibody generation and antibody origin in this invention; Figure 3 This is a diagram illustrating the dynamic updating mechanism of the antibody composition in this invention. Figure 4 This is a structural diagram of the antibody library and system modules in this invention. Detailed Implementation

[0032] The principles and features of this invention are described below. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they should be performed according to the techniques or conditions described in the literature in this field, or according to the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0033] This invention provides a universal antibody dynamic update technology platform, whose core steps (antigen preparation → antibody generation → dynamic update) are independent of specific tumor types or species. The following examples illustrate this invention using human liver cancer tissue (demonstrating clinical sample applicability) and a mouse squamous cell carcinoma model of the skin (demonstrating the effectiveness of time-series dynamic updates and in vivo efficacy verifiability), but the scope of protection of this invention is not limited thereto. The method of this invention is not limited to specific tumor types or species; those skilled in the art can select appropriate tumor sources and validation models according to actual needs.

[0034] Example 1: In vitro antibody generation and composition preparation.

[0035] This embodiment uses human liver cancer tissue as an example to illustrate that the present invention can prepare tumor antigens from clinical samples. Those skilled in the art will understand that the same method can be applied to other tumor types, including but not limited to squamous cell carcinoma of the skin used in subsequent embodiments. To verify the feasibility of the technical solution of the present invention, antitumor antibodies were prepared using an in vitro immunization method, and the specific steps are as follows: 1. Tumor antigen preparation: Samples were collected from surgically removed tumor tissue from patients with pathologically confirmed primary liver cancer. The collection period was from July 2018 to July 2020 at the Affiliated Hospital of Guilin Medical University. The samples were processed under aseptic conditions: the tumor tissue was cut into 1-3 mm pieces. 3 Tissue blocks were washed 2-3 times with phosphate-buffered saline (PBS) and then enzymatically digested to obtain a tumor cell suspension. This enzymatic digestion included, but was not limited to, treatment with trypsin or collagenase (37°C, 20-60 minutes). After digestion, the cells were filtered through a 40-100 μm cell sieve, and single-cell suspensions were collected and centrifuged at 300-500 × g for 5-10 minutes. The resulting tumor cells were inactivated using one of the following methods: gamma irradiation (20-100 Gy); mitomycin C treatment (10-50 μg / mL, 30-60 minutes); freeze-thaw cycles (2-5 times). The inactivated cells were then used for subsequent in vitro immunotherapy.

[0036] Alternatively, the inactivated tumor cells can be resuspended in membrane protein extraction buffer, sonicated on ice, and centrifuged to remove the cell nuclei. The precipitate is the tumor cell membrane component, which can be used as an in vitro immunogenic antigen. Membrane proteins can also be extracted to prepare tumor cell membrane-associated protein components for use as in vitro immunogenic antigens.

[0037] 2. B cell preparation: Peripheral blood mononuclear cells (PBMCs) were isolated from healthy donor peripheral blood: PBMCs were isolated using density gradient centrifugation and then sorted by magnetic beads. Cells. After sorting, the purity of B cells is preferably ≥90%. B cells are resuspended in culture medium (RPMI-1640 + 5-15% fetal bovine serum) at a cell density of 0.5 × 10⁻⁶ cells / year. 6 -2×10 6 cells / mL; In this embodiment, the preferred cell density is 1×10⁻⁶ cells / mL; 6 cells / mL.

[0038] 3. In vitro antibody induction: Inactivated tumor antigens are contacted with B cells in an in vitro co-culture system: the ratio of tumor cells to B cells is 1:1 to 5:1; the culture temperature is 37°C, 5% CO2; the culture time is 5-14 days. In this embodiment, the preferred cell ratio is 3:1 and the culture time is 8 days. Immunostimulatory factors, including but not limited to: IL-2 (5-50 ng / mL); IL-4 (5-50 ng / mL); IL-21 (10-100 ng / mL), preferably IL-2 (10 ng / mL); IL-4 (15 ng / mL); IL-21 (50 ng / mL); CD40 agonist or Toll-like receptor agonist, are added to the culture system every 2-3 days during the culture process.

[0039] 4. Antibody collection and purification: After incubation, the supernatant was collected and filtered through a 0.22 μm filter membrane. The IgG antibody fraction was purified using Protein A or Protein G affinity chromatography. After elution, the pH was adjusted to neutral, and buffer replacement (PBS) was performed. The purified antibody concentration was 1–20 mg / mL, with 10 mg / mL being the preferred concentration in this example.

[0040] 5. Result Verification: The binding ability of antibodies to tumor antigens was detected by ELISA: the OD value of antibodies to tumor antigens was significantly higher than that of the negative control (more than 1.5-5 times higher) (p<0.01); at the same time, the binding of antibodies to tumor cells was detected by flow cytometry: the positive rate of antibody binding to tumor cells was preferably ≥30%-80% (p<0.01).

[0041] The results showed that the obtained antibody could specifically bind to tumor-associated antigens, proving that the method of the present invention is feasible.

[0042] Hepatocellular carcinoma antigen was coated onto an ELISA plate, and purified antibody was added for incubation. Detection was performed using horseradish peroxidase (HRP)-labeled anti-human IgG secondary antibody. Pre-immunization B cell culture supernatant served as a negative control, and phosphate-buffered saline (PBS) served as a blank control. Each group had three replicates, and the experiment was repeated three times (Table 1).

[0043] Table 1. ELISA method for detecting the binding activity (OD) of antibodies to hepatocellular carcinoma antigens. 450 , (n=3) Results: The OD of the antibody prepared in Example 1 of this invention (1:100 dilution) 450The value was 0.32, which was 2.7 times that of the negative control (0.12) (p<0.01), indicating that the obtained antibody could specifically bind to liver cancer antigen.

[0044] The purified antibody was reacted with the human hepatocellular carcinoma cell line HepG2 (1×10⁻⁶). 6 The cells were co-incubated (1 cell / tube) and detected with FITC-labeled anti-human IgG secondary antibody. Pre-immunization B cell culture supernatant served as a negative control. Three samples were tested in each group, and the experiment was repeated three times (Table 2).

[0045] Table 2. Flow cytometry analysis of antibody binding rate to HepG2 cells (%) (n=3) Results: The antibody (10 μg / mL) prepared in Example 1 of this invention showed a positive binding rate of 58.3% to HepG2 cells, which was significantly higher than that of the negative control (2.1%, p<0.01), indicating that the obtained antibody can specifically recognize the surface antigen of liver cancer cells.

[0046] Example 2: Verification of the effect of dynamic time series updates.

[0047] To verify the core innovative feature of this invention—the dynamic updating mechanism of antibody compositions based on time-series antigen information—this embodiment obtains tumor-associated antigens at different time points and repeats the method of Example 1 to prepare different batches of antibody compositions, further introducing the time-series dynamic updating strategy of this invention.

[0048] To verify the in vivo therapeutic effect of the present invention, this embodiment uses a DMBA / TPA-induced mouse squamous cell carcinoma model of the skin. This model is a recognized and highly reproducible animal model in tumor research, capable of evaluating the antitumor activity of the antibody composition under controlled conditions. Those skilled in the art will understand that a technical solution validated in an animal model has the reasonable expectation of being generalizable to human tumors.

[0049] 1. Establishment of tumor model.

[0050] A mouse cutaneous squamous cell carcinoma (mCSCC) model induced by DMBA / TPA was used, with the basic model based on the applicant's published research (Liu Z). eLife2024;13:RP95678). 60 μg of 7,12-dimethylbenzo[a]anthracene (DMBA, dissolved in 200 μl acetone) was applied to the back of C57BL / 6 mice (purchased from Hunan Slack Jingda Experimental Animal Co., Ltd.) once daily for one week; subsequently, 2.5 μg of 12-O-tetradecanoylphorbol-13-acetate (phorbol ester) (TPA, dissolved in 200 μl acetone) was applied twice weekly for 10 weeks.

[0051] 2. Antigen acquisition at multiple time points.

[0052] (1) Setting the time point: Tumor tissue was collected from the backs of DMBA / TPA-induced tumor-bearing mice (C57BL / 6) at three time points (T1: week 12, T2: week 15, and T3: week 17). The tumor tissue was minced, digested with 0.25% trypsin / EDTA, filtered through a 100 μm sieve, digested with DMEM / 10% FBS, and centrifuged at 500×g for 5 minutes. Cells were resuspended in KC growth medium and cultured at 1×10⁻⁶ cells / mL. 5 / 100 mm plate, cultured at 37℃ and 5% CO2 for 7 days to obtain expanded tumor cells.

[0053] (2) Antigen preparation at each time point Following the method in step 1 of Example 1, tumor tissues collected at time points T1, T2, and T3 were subjected to cell separation and inactivation treatment to obtain three batches of tumor antigens: Ag-T1, Ag-T2, and Ag-T3.

[0054] 3. Preparation of multiple batches of antibody compositions.

[0055] Following the method in steps 2 to 4 of Example 1, Ag-T1, Ag-T2, and Ag-T3 were used as immunogenic antigens, and in vitro B cell immunization and antibody purification were performed independently to obtain three batches of antibody compositions: Ab-V1 (targeting T1 antigen), Ab-V2 (targeting T2 antigen), and Ab-V3 (targeting T3 antigen).

[0056] 4. Analysis of antigen loss, increase, or drift.

[0057] (1) Comparison of antigen profiles: Flow cytometry and mass spectrometry were used to compare the surface antigen profiles of tumor antigens at three time points: Ag-T1, Ag-T2, and Ag-T3. The results showed that Ag-T2, compared to Ag-T1, exhibited new antigen expression (new antigen appearance) and structural changes in some antigens (antigen mutations); Ag-T3, compared to Ag-T2, showed downregulation of some antigen expression (antigen loss). These results indicate that tumor antigens underwent continuous antigen loss, increase, or drift over time.

[0058] Tumor cells were collected at time points T1, T2, and T3 (1×10⁻⁶ cells per time point). 6 Each sample was incubated with specific antibodies (anti-p53, anti-Bcl-xL, anti-NF-κB, and anti-Bax) against antigens A (p53), B (Bcl-xL), C (NF-κB), and D (Bax), respectively, and detected using FITC-labeled secondary antibodies. An isotype control antibody was used as a negative control. Three samples were tested in each group, and the experiment was repeated three times (Table 3).

[0059] Table 3 Positive rates (%) of tumor cell surface antigen expression at different time points (n=3) Interpretation of results: (1) Neoantigen appearance: The positive rate of antigen A increased from 35.3% in T1 to 52.6% in T2 (p<0.05), an increase of about 1.5 times; (2) Neoantigen appearance: The positive rate of antigen B increased from 52.5% in T1 to 75.2% in T2 (p<0.05), an increase of about 1.4 times; (3) Antigen loss: The positive rate of antigen C decreased from 72.3% in T1 to 58.6% in T2 (p<0.05), a decrease of about 19%; (4) Antigen loss: The positive rate of antigen D decreased from 25.3% in T2 to 12.4% in T3 (p<0.01), a decrease of about 51%.

[0060] Tumor cell membrane proteins were extracted at time points T1, T2, and T3, and identified by liquid chromatography-tandem mass spectrometry (LC-MS / MS) after trypsin digestion. Specific peptides and proteins identified at each time point were compared using a database search (Table 4).

[0061] Table 4. Mass spectrometry analysis of changes in tumor cell membrane protein expression at different time points. Specific examples: (1) Antigen A (p53): 1 specific peptide was identified in T1, 5 in T2, and 8 in T3, with continuous upregulation of expression; (2) Antigen C (NF-κB): 7 specific peptides were identified in T1, 5 in T2, and only 2 in T3, with continuous downregulation of expression; (3) Antigen E (mutant EGFR - L861Q): No mutant peptide was detected in T1, but the L861Q mutant peptide was specifically identified in T2 / T3, indicating that the antigen structure has mutated.

[0062] (2) Antibody binding profile analysis: Binding experiments were performed on Ab-V1 with Ag-T1, Ag-T2, and Ag-T3, respectively. Ag-T1, Ag-T2, and Ag-T3 were coated onto ELISA plates (5 μg / mL, incubated overnight at 4°C). Ab-V1 antibody (10 μg / mL, incubated at 37°C for 2 hours) was added, and the OD was measured using HRP-labeled secondary antibody. 450 Value. Pre-immunization IgG served as a negative control, and PBS served as a blank control. Each group had 3 replicates, and the experiment was independently repeated 3 times. Relative binding rate was calculated using the formula: Relative binding rate (%) = (Experimental group OD) / (Pre-immunization IgG / PBS) = 0.5% / 0.5% 450 - Blank control) / (Ag-T1 group OD 450 - Blank control) × 100%. The relative binding rate of the Ag-T1 group was set at 100% (benchmark). Data are expressed as mean ± standard deviation. One-way ANOVA and Tukey multiple comparison test were used for comparisons between groups. p < 0.05 was considered statistically significant (Table 5).

[0063] Table 5 Experiments on the binding of Ab-V1 with Ag-T1, Ag-T2, and Ag-T3 The results showed that Ab-V1 had the strongest binding to Ag-T1 (relative binding rate = 100%, baseline); the binding of Ab-V1 to Ag-T2 decreased significantly (relative binding rate = 44.7% ± 8%, p < 0.01); and the binding of Ab-V1 to Ag-T3 decreased further (relative binding rate = 31.8% ± 6%, p < 0.01).

[0064] The above results indicate that the antibody composition prepared against the early antigen has a significantly reduced binding ability to the later drift antigen, verifying the limitations of the fixed-target strategy.

[0065] 5. Verify the effect of dynamic updates.

[0066] (1) Cross-combination experiment Cross-binding assays were performed on antibody compositions from different batches and antigens at different time points. Ag-T1, Ag-T2, and Ag-T3 antibodies were coated onto ELISA plates (5 μg / mL, incubated overnight at 4°C). Ab-V1, Ab-V2, and Ab-V3 antibodies (10 μg / mL each, incubated at 37°C for 2 hours) were added separately, and OD was measured using HRP-labeled secondary antibody. 450 Value. Pre-immunization IgG served as a negative control, and PBS served as a blank control. Each group had 3 replicates, and the experiment was independently repeated 3 times. Relative binding rate was calculated using the formula: Relative binding rate (%) = (Experimental group OD) / (Pre-immunization IgG / PBS) = 0.5% / 0.5% 450 - Blank control) / (OD of antigen and corresponding antibody group in the same batch) 450 - Blank control) × 100%. The relative binding rate of the same batch of antigen to the corresponding antibody group was set at 100% (benchmark value). Data are expressed as mean ± standard deviation. Two-way ANOVA and Tukey's multiple comparison test were used for comparisons between groups. p < 0.05 was considered statistically significant. The results are shown in Table 6 below: Table 6. Antigen-antibody cross-binding experiments at different time points As shown in Table 6, the binding rate of Ab-V2 to Ag-T2 (100%) was significantly higher than that of Ab-V1 to Ag-T2 (45%), indicating that the updated antibody composition could better recognize the drifted tumor antigen; the binding rate of Ab-V3 to Ag-T3 (100%) was significantly higher than that of Ab-V2 to Ag-T3 (58%), further verifying the necessity of continuous updating; the antibody compositions prepared at different time points showed significant differences in antigen recognition characteristics, indicating that the antibody compositions could be dynamically adjusted according to changes in tumor antigens.

[0067] Example 3: Validation of the therapeutic effect of dynamically updated antibody compositions.

[0068] To verify the technical effectiveness of the antibody dynamic update strategy of this invention, this embodiment conducts an improved and comparative experiment based on an existing mouse cutaneous squamous cell carcinoma (mCSCC) serum antibody treatment model. The basic model is based on the applicant's published research (Liu Z). eLife (2024;13:RP95678), and further introduces the dynamic update technology solution of the present invention.

[0069] To verify the in vivo therapeutic effect of the present invention, this embodiment uses a DMBA / TPA-induced mouse squamous cell carcinoma model of the skin. This model is a recognized and highly reproducible animal model in tumor research, capable of evaluating the antitumor activity of the antibody composition under controlled conditions. Those skilled in the art will understand that a technical solution validated in an animal model has the reasonable expectation of being generalizable to human tumors.

[0070] 1. Establishment of the basic model.

[0071] (1) Tumor induction: A mouse cutaneous squamous cell carcinoma (mCSCC) model induced by DMBA / TPA was used. C57BL / 6 mice (purchased from Hunan Slack Jingda Experimental Animal Co., Ltd.) were treated with 60 μg of 7,12-dimethylbenzo[a]anthracene (DMBA, dissolved in 200 μl acetone) on their backs once daily for one week; subsequently, 2.5 μg of 12-O-tetradecanoylphorbol-13-acetate (phorbol ester) (TPA, dissolved in 200 μl acetone) was applied twice weekly for 10 weeks. Tumor volume was measured weekly using the formula: V = π × (d 2 ×D) / 6.

[0072] (2) Tumor cell isolation and expansion: Tumor tissue was collected at week 12 when the tumor volume reached its peak. The tumor tissue was minced, digested with 0.25% trypsin / EDTA, filtered through a 100 μm sieve, and digestion was terminated with DMEM / 10% FBS. The cells were then centrifuged at 500×g for 5 minutes. The cells were resuspended in KC growth medium at 1×10⁻⁶ cells / day. 5 / 100 mm plate, cultured at 37℃ and 5% CO2 for 7 days to obtain expanded tumor cells.

[0073] (3) Preparation of initial antibody serum: 5×10 5 Amplified tumor cells were injected into healthy C57BL / 6 mice via tail vein injection (serum donor group). Seven days post-immunization, tail vein blood was collected, and serum was separated by centrifugation to serve as the initial time point antibody serum (V1).

[0074] 2. Antibody preparation expansion.

[0075] Based on the above basic model, the dynamic update strategy of this invention is introduced: (1) Antigen acquisition at multiple time points: Tumor tissues were collected at three time points (T1, T2, and T3), with an interval of 2-3 weeks: T1: Week 12; T2: Week 15; T3: Week 17. (Note: This 2-3 week interval was implemented in a mouse squamous cell carcinoma model of skin. Depending on the tumor type and clinical needs, serum antigen markers can be detected periodically, and the interval can be set accordingly. Preferably, a new round of antibody production is automatically triggered when the proportion of neoantigens exceeds 20% or the binding rate of existing target antibodies decreases by more than 20%.) At each time point, tumor cells were isolated and inactivated according to the method in step 1 of Example 1 to obtain three batches of tumor antigens (Ag-T1, Ag-T2, Ag-T3).

[0076] (2) Preparation of multiple batches of antibodies / serum: Healthy mice (n=3 per group) were immunized with Ag-T1, Ag-T2, and Ag-T3 antibodies, respectively. Three batches of antibody serum were prepared according to the method described in Part 1 above: V1: serum prepared against T1 antigen; V2: serum prepared against T2 antigen; V3: serum prepared against T3 antigen. (3) Preparation of single antibody (for control): IgG antibodies were purified from the above serum by Protein A affinity chromatography, or single antibody clones were screened by hybridoma technology, or Cosibelimab antibody was used as a monoclonal antibody control group.

[0077] 3. Experimental Grouping: Tumor-bearing mice (n=40) were randomly divided into the following 4 groups, with 10 mice in each group, as shown in Table 7 below: Table 7 Group setup and treatment regimen for in vivo drug efficacy experiments 4. Administration method: All groups were administered via tail vein injection, with each injection containing 0.02 mL (serum group) or an equivalent concentration of purified antibody (monoclonal antibody group). Dosing occurred at weeks 12, 15, and 17 (for dynamically updated groups, batches were changed according to the sequential protocol).

[0078] 5. Testing indicators: (1) Tumor volume monitoring: Tumor volume was measured twice a week, and the mean and standard deviation of tumor volume for each group were calculated.

[0079] (2) Molecular marker detection (ELISA) Animals were euthanized after treatment, and tumor tissue was collected. The expression levels of p53, Bcl-xL, NF-κB, and Bax proteins in the tumor tissue were detected using an ELISA kit. The relative expression levels of each protein (relative to the control group) were calculated using GAPDH as an internal control.

[0080] 6. Experimental results.

[0081] (1) Changes in tumor volume: The changes in tumor volume in each group are shown in Table 8 below. Comparison of tumor volume (mm) at different time points in each group of mice. 3 , ).

[0082] Table 8. Inhibition of tumor volume in tumor-bearing mice by different treatment strategies *p<0.05, **p<0.01 As shown in Table 8, the tumor volume in the dynamic update group (G1) continued to shrink after treatment, from 13.5 mm at week 12. 3 It dropped to 7.8 mm at week 17. 3 (p<0.01); The static serum group (G2) showed a certain tumor-suppressing effect at weeks 15-16 (13.2→11.5mm). 3 (p<0.05), but the effect after week 17 was not as good as the dynamic group (rebounded to 12.5 mm). 3 The monoclonal antibody group (G3) showed limited tumor suppression, with no significant difference compared to the control group (p>0.05); the tumor suppression effect of the dynamic update group was significantly better than that of the static serum group.

[0083] (2) Changes in molecular markers: The expression levels of p53, Bcl-xL, NF-κB, and Bax in each group are shown in Table 9 below. Comparison of expression levels of tumor tissue molecular markers in each group (relative to the control group) ).

[0084] Table 9. Effects of different treatment strategies on the expression of p53, Bcl-xL, NF-κB, and Bax in tumor tissues. *p<0.05, **p<0.01 As shown in Table 9, the expression levels of p53, Bcl-xL, and NF-κB in the dynamic update group (G1) decreased most significantly, while the expression level of Bax increased most significantly. The static serum group (G2) also showed some reversal of molecular markers, but the effect was weaker than that in the dynamic update group. The changes in molecular markers in the monoclonal antibody group (G3) were not significant (p>0.05).

[0085] The above experimental results demonstrate the effectiveness of the dynamic update strategy: different batches of antibody compositions prepared based on time-series antigen information, administered sequentially (dynamic update group), were significantly superior to the static serum group and the monoclonal antibody group in inhibiting tumor growth and regulating tumor-related molecular markers. Limitations of the static strategy: the static serum group showed a declining trend in tumor suppression efficacy in the later stages of treatment (from 11.5 mm at week 16). 3 Rebounded to 12.5 mm after week 17. 3 This is consistent with the decreased therapeutic effect caused by the loss, increase, or drift of tumor antigens. The advantage of multi-antibody combinations: Multi-antibody compositions (serum group) are superior to single-antibody groups in inhibiting tumor growth, indicating that multi-antibody combinations have a synergistic effect due to their broader antigen coverage.

[0086] Example 4 This embodiment relates to an antibody dynamic update system for implementing the aforementioned antibody composition dynamic update method. Figure 1 ),include: S1: Antigen acquisition module, used to acquire tumor-related antigen samples from tumors at different time points, and obtain multiple batches of antigens at different time points that reflect the changes of tumor antigens over time; S2: Antibody generation module, used to induce antibody production using multiple batches of the antigen at different time points to obtain a set of multiple batches of antibodies corresponding to different time points; Antibody generation and origin integration structures (e.g.) Figure 2 The system includes: an in vitro antibody generation pathway (201), an in vivo antibody generation pathway (202), an antibody source integration module (203), an antibody screening unit (204), and an antibody combination construction unit (205). Specifically: the in vitro pathway (201) stimulates immune cells to produce antibodies in vitro using tumor antigens; the in vivo pathway (202) obtains antibodies through in vivo immune responses; the integration module (203) aggregates antibodies from different sources; the screening unit (204) screens for antibodies with tumor antigen binding capabilities; and the construction unit (205) forms a multi-antibody composition.

[0087] S3: Antibody screening and enrichment module, used to screen and enrich antibody compositions that can specifically bind to tumor-associated antigens at corresponding time points from the antibody sets in each batch, and to construct antibody compositions targeting each time point respectively; S4: Antibody Combination Construction Module, used to dynamically update the antibody composition constructed at the previous time point based on the tumor antigen characteristics at least at the later time point according to the time series, so as to obtain an antibody composition adapted to the current tumor antigen characteristics; the formulation of the polyclonal antibody cocktail is optimized by the following methods: (a) numbering all dominant antibody clones obtained by screening in step S3 as Ab1, Ab2...Abn; (b) using orthogonal experimental design or dose matrix method to combine each antibody; (c) detecting the in vitro binding rate, ADCC activity and proliferation inhibition rate of different combinations on tumor cells; (d) screening out the combination with the highest synergistic effect as the final cocktail formulation, wherein the synergistic effect is defined as the actual inhibitory effect when used in combination being significantly greater than the expected summation effect when each antibody is used alone. Preferably, the Bliss Independence model or the Chou-Talalay combination index method is used for quantitative determination; alternatively, any one or more of the following methods can be used for determination: equivalence line graph analysis, analysis of variance, relative effect difference method or dose matrix method.

[0088] S5: Dynamic update decision module, used to repeat steps (1) to (4) sequentially at new different time points based on time series, to obtain a dynamically updated antibody composition adapted to the current tumor antigen characteristics.

[0089] In this preferred embodiment, the antibody composition exhibits a dynamic updating mechanism over time. Figure 3 The system includes: tumor antigen status at time point T1 (301), tumor antigen status at time point T2 (302), antibody composition V1 (303), antibody composition V2 (304), and an update adjustment module (305). Specifically: V1 is generated based on the T1 antigen status; V2 adjusts the structure of V1 based on the T2 antigen status; adjustments include antibody replacement, addition, or proportional correction; enabling adaptive updating of the antibody composition as the antigen changes over time.

[0090] The preferred antibody dynamic update system and antibody library structure in this embodiment include: (I) System modules: antigen acquisition module (401), antibody generation module (402), antibody screening module (403), antibody combination construction module (404), and dynamic update decision module (405); (II) Antibody library structure: antibody sequence information module (406), antigen source information module (407), time tag information module (408), and antibody function annotation module (409). Among them, the dynamic update decision module (405) adjusts the antibody composition based on antigen change information at different time points; the antibody library is used to support antibody tracking, screening, and update optimization.

[0091] In this preferred embodiment, the adjustments performed by the dynamic update decision module include replacing some antibodies in the antibody composition, increasing antibodies against newly emerging or upregulated antigens, and deleting or reducing the proportion of antibodies against antigens whose expression levels are downregulated or have disappeared.

[0092] Furthermore, the timing of the dynamic update of the antibody composition is not limited to a fixed time interval, but can also be event-driven by monitoring real-time changes in tumor antigens. Specifically, peripheral blood of patients is collected periodically, and tumor antigens in the serum are detected. High-throughput sequencing or flow cytometry is used to analyze the frequency of neoantigens and the proportion of lost original antigens. Preferably, a new round of antibody composition preparation and reinfusion is initiated when any of the following triggering conditions are met: (a) the detection frequency of neoantigens exceeds 20% of the total antigen spectrum; (b) the antibody binding rate of any epitope in the original antigen decreases by more than 20% compared to the previous treatment cycle; (c) the tumor volume increases by more than 20% compared to the baseline. The thresholds can be adjusted by the clinician according to the specific patient condition.

[0093] In summary, the time-series antigen-driven dynamic update mechanism introduced in this invention can effectively reduce the risk of decreased therapeutic efficacy due to tumor antigen loss, increase, or drift, and maintain sustained and stable anti-tumor activity. The technical solution of this invention has superior effects in inhibiting tumor growth and regulating tumor-related molecules, verifying the technical advantages of dynamically updating antibody compositions based on time-series antigen information.

[0094] 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 method for dynamically updating antibody compositions based on time-series antigen information, characterized in that, Includes the following steps: (1) Obtain tumor-related antigen samples from tumors at different time points to obtain multiple batches of antigens at different time points that reflect the changes of tumor antigens over time; (2) Antibodies were induced by multiple batches of the antigen at different time points to obtain antibody sets at different time points; (3) Screen and enrich antibody compositions that can specifically bind to tumor-associated antigens at corresponding time points from the antibody sets of each batch, and construct antibody compositions targeting each time point respectively; (4) Based on the time series, the antibody composition constructed at the previous time point is dynamically updated according to the tumor antigen characteristics at least at the later time point to obtain an antibody composition adapted to the current tumor antigen characteristics. (5) Repeat steps (1) to (4) sequentially at new different time points based on the time series to obtain a dynamically updated antibody composition adapted to the current tumor antigen characteristics.

2. The method for dynamically updating antibody compositions based on time-series antigen information according to claim 1, characterized in that, In step (1), the antigen sample includes at least one of tumor cells, tumor cell membrane-associated protein components, tumor-derived proteins, and tumor-derived peptides. The method of inducing antibody production in step (2) includes at least one of in vitro antibody production and in vivo antibody production. The in vitro antibody generation method includes: culturing the tumor-associated antigen in contact with immune cells derived from a healthy donor in vitro, and inducing B cells to produce antibodies under immune stimulation conditions; The method of generating antibodies in vivo includes: introducing the tumor-associated antigen into a non-tumor body, inducing an immune response in the body, and then obtaining antibodies.

3. The method for dynamically updating antibody compositions based on time-series antigen information according to claim 1, characterized in that, The antibody screening methods described in step (3) include at least one of antigen binding ability screening, antibody display technology screening, and polyclonal composition analysis based on antibody sequence information.

4. The method for dynamically updating antibody compositions based on time-series antigen information according to claim 1, characterized in that, The antibody composition in step (3) is at least one of a polyclonal antibody mixture, an oligoclonal antibody composition, and a recombinant expression antibody composition; the antibody composition contains multiple antibodies with different antigen-binding properties, used to simultaneously recognize multiple different antigenic determinants on the surface of tumor cells.

5. The method for dynamically updating antibody compositions based on time-series antigen information according to claim 1, characterized in that, The dynamic update described in step (4) includes at least one of the following operations (a) to (c): (a) Replacing a portion of the antibody in the antibody composition; (b) Increase antibodies against newly emerging or upregulated antigens; (c) Delete or reduce the proportion of antibodies against antigens whose expression levels are downregulated or disappear.

6. A method for dynamically updating antibody compositions based on time-series antigen information according to any one of claims 1 to 5, characterized in that, It also includes an antibody library establishment step: sequencing the obtained antibody sets from multiple batches at different time points and establishing an antibody database; the antibody database includes at least antibody sequence information, antigen binding characteristic information, and antigen source information at the corresponding time points.

7. An antibody dynamic update system for implementing the antibody composition dynamic update method according to any one of claims 1 to 6, characterized in that, include: S1: Antigen acquisition module, used to acquire tumor-related antigen samples from tumors at different time points, and obtain multiple batches of antigens at different time points that reflect the changes of tumor antigens over time; S2: Antibody generation module, used to induce antibody production using multiple batches of the antigen at different time points to obtain a set of multiple batches of antibodies corresponding to different time points; S3: Antibody screening and enrichment module, used to screen and enrich antibody compositions that can specifically bind to tumor-associated antigens at corresponding time points from the antibody sets in each batch, and to construct antibody compositions targeting each time point respectively; S4: Antibody composition construction module, used to dynamically update the antibody composition constructed at the previous time point based on the tumor antigen characteristics at least at the later time point according to the time series, so as to obtain an antibody composition adapted to the current tumor antigen characteristics; S5: Dynamic update decision module, used to repeat steps (1) to (4) sequentially at new different time points based on time series, to obtain a dynamically updated antibody composition adapted to the current tumor antigen characteristics.

8. The antibody dynamic updating system according to claim 7, characterized in that, The adjustments performed by the dynamic update decision module include replacing some antibodies in the antibody composition, increasing antibodies against newly emerging or upregulated antigens, and deleting or reducing the proportion of antibodies against antigens whose expression levels are downregulated or have disappeared.

9. An antibody composition based on time-series antigen information, characterized in that, The antibody composition is prepared by the antibody composition dynamic update method according to any one of claims 1 to 6, or updated by the antibody dynamic update system according to claim 7 or 8.

10. The method for dynamically updating an antibody composition based on time-series antigen information according to any one of claims 1 to 6, the antibody dynamic updating system according to claim 7 or 8, or the use of the antibody composition according to claim 9 in the preparation of a medicament for treating tumors.

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